3D ultrasound-based instrument for non-invasive measurement of amniotic fluid volume Number:7,520,857 from the United States Patent and Trademark Office (PTO) owispatent A hand-held 3D ultrasound instrument is disclosed which is used to non-invasively and automatically measure amniotic fluid volume in the uterus requiring a minimum of operator intervention. Using a 2D image-processing algorithm, the instrument gives automatic feedback to the user about where to acquire the 3D image set. The user acquires one or more 3D data sets covering all of the amniotic fluid in the uterus and this data is then processed using an optimized 3D algorithm to output the total amniotic fluid volume corrected for any fetal head brain volume contributions.<H measurement, instrument, 3D, ultrasound, b, 7520857.html, Patent, pto, 7520857

Title: 3D ultrasound-based instrument for non-invasive measurement of amniotic fluid volume

Abstract: A hand-held 3D ultrasound instrument is disclosed which is used to non-invasively and automatically measure amniotic fluid volume in the uterus requiring a minimum of operator intervention. Using a 2D image-processing algorithm, the instrument gives automatic feedback to the user about where to acquire the 3D image set. The user acquires one or more 3D data sets covering all of the amniotic fluid in the uterus and this data is then processed using an optimized 3D algorithm to output the total amniotic fluid volume corrected for any fetal head brain volume contributions.

Patent Number: 7,520,857 Issued on 04/21/2009 to Chalana, et al.

Inventors: Chalana; Vikram (Mill Creek, WA), Dudycha; Stephen (Bothell, WA), McMorrow; Gerald (Kirkland, WA)
Assignee: Verathon Inc. (Bothell, WA)

Appl. No.: 11/119,355

Filed: April 29, 2005

Related U.S. Patent Documents


Application Number	Filing Date	Patent Number	Issue Date
10701955	Nov., 2003	7087022
10443126	May., 2003	7041059
11119355
PCT/US2003/24368	Aug., 2003
PCT/US2003/14785	May., 2003
10165556	Jun., 2002	6676605
11119355
10633186	Jul., 2003	7004904
60556823	Apr., 2004
60423881	Nov., 2002
60400624	Aug., 2002
60470525	May., 2003

Current U.S. Class: 600/446 ; 600/437; 600/443; 600/449

Current International Class: A61B 8/00 (20060101); A61B 8/02 (20060101); A61B 8/06 (20060101); A61B 8/12 (20060101); A61B 8/14 (20060101)

Field of Search: 382/128 600/437,443,446,456,458,459,439

References Cited [Referenced By]

U.S. Patent Documents


7041059	May 2006	Chalana et al.
7087022	August 2006	Chalana et al.
2002/0133075	September 2002	Abdelhak
2004/0024302	February 2004	Chalana et al.

Other References

Elliot, P.J., Interactive image segmentation for radiation treatment planning, IBM Sys J., 92:12, pp. 620-634. cited by other.

Primary Examiner: Mercader; Eleni Mantis
Assistant Examiner: Jasani; Ashish
Attorney, Agent or Firm: Black; Richard T. Black Lowe & Graham

Parent Case Text

PRIORITY CLAIM

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 claims priority to U.S. provisional patent application Ser. No. 60/566,823 filed Apr. 30, 2004.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 claims priority to and is a continuation-in-part of U.S. patent application Ser. No. 10/701,955 filed Nov. 5, 2003, now U.S. Pat. No. 7,087,022 which in turn claims priority to and is a continuation-in-part of U.S. patent application Ser. No. 10/443,126 filed May 20, 2003 now U.S. Pat. No. 7,041,059.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 is a continuation-in-part of and claims priority to PCT application serial number PCT/US03/24368 filed Aug. 1, 2003, which claims priority to U.S. provisional patent application Ser. No. 60/423,881 filed Nov. 5, 2002 and U.S. provisional patent application Ser. No. 60/400,624 filed Aug. 2, 2002.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 is also a continuation-in-part of and claims priority to PCT Application Serial No. PCT/US03/14785 filed May 9, 2003, which is a continuation of U.S. patent application Ser. No. 10/165,556 filed Jun. 7, 2002.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 is also a continuation-in-part of and claims priority to U.S. patent application Ser. No. 10/633,186 filed Jul. 7, 2003 which claims priority to U.S. provisional patent application Ser. No. 60/423,881 filed Nov. 5, 2002 and U.S. provisional patent application Ser. No. 60/423,881 filed Aug. 2, 2002, and to U.S. patent application Ser. No. 10/443,126 filed May 20, 2003 which claims priority to U.S. provisional patent application Ser. No. 60/423,881 filed Nov. 5, 2002 and to U.S. provisional application 60/400,624 filed Aug. 2, 2002.

This U.S. patent application Ser. No. 11/119,355 filed Apr. 29, 2005 also claims priority to U.S. provisional patent application Ser. No. 60/470,525 filed May 12, 2003, and to U.S. patent application Ser. No. 10/165,556 filed Jun. 7, 2002. All of the above applications are herein incorporated by reference in their entirety as if fully set forth herein.

Claims

We claim:

1. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second portion of the uterus to obtain a second plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first plurality with the second plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.

2. The method of claim 1, wherein plurality of scanplanes are acquired from a rotational array, a translational array, or a wedge array.

3. The method of claim 1, wherein the plurality of algorithms includes algorithms for image enhancement, segmentation, and polishing.

4. The method of claim 3, wherein segmentation further includes an intensity clustering step, a spatial gradients step, a hysteresis threshold step, a Region-of-Interest selection step, and a matching edges filter step.

5. The method of claim 4, wherein the intensity clustering step is performed in a first parallel operation, and the spatial gradients, hysteresis threshold, Region-of-Interest selection, and matching edges filter steps are performed in a second parallel operation, and further wherein the results from the first parallel operation are combined with the results from the second parallel operation.

6. The method of claim 3, wherein image enhancement further includes applying a heat filter and a shock filter to the digital images.

7. The method of claim 6 wherein the heat filter is applied to the digital images followed by application of the shock filter to the digital images.

8. The method of claim 1, wherein the amniotic fluid volume is adjusted for underestimation or overestimation.

9. The method of claim 8, wherein the amniotic fluid volume is adjusted for underestimation by probing with adjustable ultrasound frequencies to penetrate deep tissues and to repositioning the transceiver to establish that deep tissues are exposed with probing ultrasound of sufficient strength to provide a reflecting ultrasound echo receivable by the transceiver, such that more than one rotational array to detect deep tissue and regions of the fetal head are obtained.

10. The method of claim 8, wherein amniotic fluid volume is adjusted for overestimation by automatically determining fetal head volume contribution to amniotic fluid volume and deducting it from the amniotic fluid volume.

11. The method of claim 10, wherein the steps to adjust for overestimated amniotic fluid volumes include a 2D clustering step, a matching edges step, an all edges step, a gestational age factor step, a head diameter step, an head edge detection step, and a Hough transform step.

12. The method of claim 11, wherein the Hough transform step includes a polar Hough Transform step, a Find Maximum Hough value step, and a fill circle region step.

13. The method of claim 12, wherein the polar Hough Transform step includes a first Hough transform to look for lines of a specified shape, and a second Hough transform to look for fetal head structures.

14. The method of claim 1, wherein the positions include lateral and transverse.

15. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second and a third portion of the uterus to obtain a second and third plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first plurality through the third plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.

16. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second through fourth portion of the uterus to obtain a second through fourth plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first through fourth plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.

17. A method to determine amniotic fluid volume in digital images, the method comprising: positioning an ultrasound transceiver to probe a first portion of a uterus of a patient, the transceiver adapted to obtain a first plurality of scanplanes; re-positioning the ultrasound transceiver to probe a second through fifth portion of the uterus to obtain a second through fifth plurality of scanplanes; enhancing the images of the amniotic fluid regions in the scanplanes with a plurality of algorithms; registering the scanplanes of the first through the fifth plurality; associating the registered scanplanes into a composite array, and determining the amniotic fluid volume of the amniotic fluid regions within the composite array.

18. A system for determining amniotic fluid volume, the system comprising: a transceiver positioned from two to six locations of a patient, the transceiver configured to deliver radio frequency ultrasound pulses to amniotic fluid regions of a patient, to receive echoes of the pulses reflected from the amniotic fluid regions, to convert the echoes to digital form, and to obtain a plurality of scanplanes in the form of an array for each location; a computer system in communication with the transceiver, the computer system having a microprocessor and a memory, the memory further containing stored programming instructions operable by the microprocessor to associate the plurality of scanplanes of each array, and the memory further containing instructions operable by the microprocessor to determine the presence of an amniotic fluid region in each array and determine the amniotic fluid volume in each array.

19. The system of claim 18, wherein the array includes rotational, wedge, and translation.

20. The system of claim 18, wherein stored programming instructions further include aligning scanplanes having overlapping regions from each location into a plurality of registered composite scanplanes.

21. The system of claim 20, wherein the stored programming instructions further include fusing the registered composite scanplanes amniotic fluid regions of the scanplanes of each array.

22. The system of claim 21 wherein the stored programming instructions further include arranging the fused composite scanplanes into a composite array.

23. The system of claim 18, wherein the computer system is configured for remote operation via a local area network or an Internet web-based system, the internet web-based system having a plurality of programs that collect, analyze, and store amniotic fluid volume.

Description

FIELD OF THE INVENTION

This invention pertains to the field of obstetrics, particularly to ultrasound-based non-invasive obstetric measurements.

BACKGROUND OF THE INVENTION

Measurement of the amount of Amniotic Fluid (AF) volume is critical for assessing the kidney and lung function of a fetus and also for assessing the placental function of the mother. Amniotic fluid volume is also a key measure to diagnose conditions such as polyhydramnios (too much AF) and oligohydramnios (too little AF). Polyhydramnios and oligohydramnios are diagnosed in about 7-8% of all pregnancies and these conditions are of concern because they may lead to birth defects or to delivery complications. The amniotic fluid volume is also one of the important components of the fetal biophysical profile, a major indicator of fetal well-being.

The currently practiced and accepted method of quantitatively estimating the AF volume is from two-dimensional (2D) ultrasound images. The most commonly used measure is known as the use of the amniotic fluid index (AFI). AFI is the sum of vertical lengths of the largest AF pockets in each of the 4 quadrants. The four quadrants are defined by the umbilicus (the navel) and the linea nigra (the vertical mid-line of the abdomen). The transducer head is placed on the maternal abdomen along the longitudinal axis with the patient in the supine position. This measure was first proposed by Phelan et al (Phelan J P, Smith C V, Broussard P, Small M., "Amniotic fluid volume assessment with the four-quadrant technique at 36-42 weeks' gestation," J Reprod Med Jul; 32(7): 540-2, 1987) and then recorded for a large normal population over time by Moore and Cayle (Moore T R, Cayle J E. "The amniotic fluid index in normal human pregnancy," Am J Obstet Gynecol May; 162(5): 1168-73, 1990).

Even though the AFI measure is routinely used, studies have shown a very poor correlation of the AFI with the true AF volume (Sepulveda W, Flack N J, Fisk N M., "Direct volume measurement at midtrimester amnioinfusion in relation to ultrasonographic indexes of amniotic fluid volume," Am J Obstet Gynecol Apr; 170(4): 1160-3, 1994). The correlation coefficient was found to be as low as 0.55, even for experienced sonographers. The use of vertical diameter only and the use of only one pocket in each quadrant are two reasons why the AFI is not a very good measure of AF Volume (AFV). Some of the other methods that have been used to estimate AF volume include:

Dye dilution technique. This is an invasive method where a dye is injected into the AF during amniocentesis and the final concentration of dye is measured from a sample of AF removed after several minutes. This technique is the accepted gold standard for AF volume measurement; however, it is an invasive and cumbersome method and is not routinely used.

Subjective interpretation from ultrasound images. This technique is obviously dependent on observer experience and has not been found to be very good or consistent at diagnosing oligo- or poly-hydramnios.

Vertical length of the largest single cord-free pocket. This is an earlier variation of the AFI where the diameter of only one pocket is measured to estimate the AF volume.

Two-diameter areas of the largest AF pockets in the four quadrants. This is similar to the AFI; however, in this case, two diameters are measured instead of only one for the largest pocket. This two diameter area has been recently shown to be better than AFI or the single pocket measurement in identifying oligohydramnios (Magann E F, Perry K G Jr, Chauhan S P, Anfanger P J, Whitworth N S, Morrison J C., "The accuracy of ultrasound evaluation of amniotic fluid volume in singleton pregnancies: the effect of operator experience and ultrasound interpretative technique," J Clin Ultrasound, Jun; 25(5):249-53, 1997).

See also: U.S. Pat. No. 6,346,124 to Geiser, et al. (Autonomous Boundary Detection System For Echocardiographic Images). Similarly, the measurement of bladder structures are covered in U.S. Pat. No. 6,213,949 to Ganguly, et al. (System For Estimating Bladder Volume) and U.S. Pat. No. 5,235,985 to McMorrow, et al., (Automatic Bladder Scanning Apparatus). The measurement of fetal head structures is described in U.S. Pat. No. 5,605,155 to Chalana, et al., (Ultrasound System For Automatically Measuring Fetal Head Size). The measurement of fetal weight is described in U.S. Pat. No. 6,375,616 to Soferman, et al. (Automatic Fetal Weight Determination), Segiv et al. (in Segiv C, Akselrod S, Tepper R., "Application of a semiautomatic boundary detection algorithm for the assessment of amniotic fluid quantity from ultrasound images", Ultrasound Med Biol, May; 25(4): 515-26, 1999), Grover et al. (Grover J, Mentakis E A, Ross M G, "Three-dimensional method for determination of amniotic fluid volume in intrauterine pockets."Obstet Gynecol, Dec; 90(6): 1007-10, 1997). None of the currently used methods for AF volume estimation are ideal. Therefore, there is a need for better, non-invasive, and easier ways to accurately measure amniotic fluid volume.

SUMMARY OF THE INVENTION

A preferred form of the invention utilizes a three dimensional (3D) ultrasound-based system and method preferably using a hand-held 3D ultrasound device to acquire at least one 3D data set of a uterus and having one or more, or preferably a plurality of automated processes optimized to accurately and precisely locate and measure the volume of amniotic fluid in the uterus without resorting to pre-supposed models of the shapes of amniotic fluid pockets in ultrasound images. The automated process uses one or more, or preferably a plurality, of algorithms, preferably in a sequence that includes steps for image enhancement, segmentation, and polishing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a side view of a microprocessor-controlled, hand-held ultrasound transceiver;

FIG. 2A is a is depiction of the hand-held transceiver in use for scanning a patient;

FIG. 2B is a perspective view of the hand-held transceiver device sitting in a communication cradle;

FIG. 2C is a perspective view of an amniotic fluid volume measuring system;

FIG. 3 is an alternate embodiment of an amniotic fluid volume measuring system in schematic view of a plurality of transceivers in connection with a server;

FIG. 4 is another alternate embodiment of an amniotic fluid volume measuring system in a schematic view of a plurality of transceivers in connection with a server over a network;

FIG. 5A a graphical representation of a plurality of scan lines forming a single scan plane;

FIG. 5B is a graphical representation of a plurality of scanplanes forming a three-dimensional array having a substantially conic shape;

FIG. 5C is a graphical representation of a plurality of 3D distributed scanlines emanating from the transceiver forming a scancone;

FIG. 6 is a depiction of the hand-held transceiver placed laterally on a patient trans-abdominally to transmit ultrasound and receive ultrasound echoes for processing to determine amniotic fluid volumes;

FIG. 7 shows a block diagram overview of the two-dimensional and three-dimensional Input, Image Enhancement, Intensity-Based Segmentation, Edge-Based Segmentation, Combine, Polish, Output, and Compute algorithms to visualize and determine the volume or area of amniotic fluid;

FIG. 8A depicts the sub-algorithms of Image Enhancement;

FIG. 8B depicts the sub-algorithms of Intensity-Based Segmentation;

FIG. 8C depicts the sub-algorithms of Edge-Based Segmentation;

FIG. 8D depicts the sub-algorithms of the Polish algorithm, including Close, Open, Remove Deep Regions, and Remove Fetal Head Regions;

FIG. 8E depicts the sub-algorithms of the Remove Fetal Head Regions sub-algorithm;

FIG. 8F depicts the sub-algorithms of the Hough Transform sub-algorithm;

FIG. 9 depicts the operation of a circular Hough transform algorithm;

FIG. 10 shows results of sequentially applying the algorithm steps on a sample image;

FIG. 11 illustrates a set of intermediate images of the fetal head detection process;

FIG. 12 presents a 4-panel series of sonographer amniotic fluid pocket outlines and the algorithm output amniotic fluid pocket outlines;

FIG. 13 illustrates a 4-quadrant supine procedure to acquire multiple image cones;

FIG. 14 illustrates an in-line lateral line procedure to acquire multiple image cones;

FIG. 15 is a block diagram overview of the rigid registration and correcting algorithms used in processing multiple image cone data sets;

FIG. 16 is a block diagram of the steps in the rigid registration algorithm;

FIG. 17A is an example image showing a first view of a fixed scanplane;

FIG. 17B is an example image showing a second view view of a moving scanplane having some voxels in common with the first scanplane;

FIG. 17C is a composite image of the first (fixed) and second (moving) images;

FIG. 18A is an example image showing a first view of a fixed scanplane;

FIG. 18B is an example image showing a second view of a moving scanplane having some voxels in common with the first view and a third view;

FIG. 18C is a third view of a moving scanplane having some voxels in common with the second view;

FIG. 18D is a composite image of the first (fixed), second (moving), and third (moving) views;

FIG. 19 illustrates a 6-section supine procedure to acquire multiple image cones around the center point of uterus of a patient in a supine procedure;

FIG. 20 is a block diagram algorithm overview of the registration and correcting algorithms used in processing the 6-section multiple image cone data sets depicted in FIG. 19;

FIG. 21 is an expansion of the Image Enhancement and Segmentation block 1010 of FIG. 20;

FIG. 22 is an expansion of the RigidRegistration block 1014 of FIG. 20;

FIG. 23 is a 4-panel image set that shows the effect of multiple iterations of the heat filter applied to an original image;

FIG. 24 shows the affect of shock filtering and a combination heat-and-shock filtering to the pixel values of the image;

FIG. 25 is a 7-panel image set progressively receiving application of the image enhancement and segmentation algorithms of FIG. 21;

FIG. 26 is a pixel difference kernel for obtaining X and Y derivatives to determine pixel gradient magnitudes for edge-based segmentation; and

FIG. 27 is a 3-panel image set showing the progressive demarcation or edge detection of organ wall interfaces arising from edge-based segmentation algorithms.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Preferably, a hand-held 3D ultrasound device is used to image the uterus trans-abdominally. The user moves the device around on the maternal abdomen and, using 2D image processing to locate the amniotic fluid areas, the device gives feedback to the user about where to acquire the 3D image data sets. The user acquires one or more 3D image data sets covering all of the amniotic fluid in the uterus and the data sets are then stored in the device or transferred to a host computer.

The 3D ultrasound device is configured to acquire the 3D image data sets in two formats. The first format is a collection of two-dimensional scanplanes, each scanplane being separated from the other and representing a portion of the uterus being scanned. Each scanplane is formed from one-dimensional ultrasound A-lines confined within the limits of the 2D scanplane. The 3D data sets is then represented as a 3D array of 2D scanplanes. The 3D array of 2D scanplanes is an assembly of scanplanes, and may be assembled into a translational array, a wedge array, or a rotatational array.

Alternatively, the 3D ultrasound device is configured to acquire the 3D image data sets from one-dimensional ultrasound A-lines distributed in 3D space of the uterus to form a 3D scancone of 3D-distributed scanline. The 3D scancone is not an assembly of 2D scanplanes.

The 3D image datasets, either as discrete scanplanes or 3D distributed scanlines, are then subjected to image enhancement and analysis processes. The processes are either implemented on the device itself or are implemented on the host computer. Alternatively, the processes can also be implemented on a server or other computer to which the 3D ultrasound data sets are transferred.

In a preferred image enhancement process, each 2D image in the 3D dataset is first enhanced using non-linear filters by an image pre-filtering step. The image pre-filtering step includes an image-smoothing step to reduce image noise followed by an image-sharpening step to obtain maximum contrast between organ wall boundaries.

A second process includes subjecting the resulting image of the first process to a location method to identify initial edge points between amniotic fluid and other fetal or maternal structures. The location method automatically determines the leading and trailing regions of wall locations along an A-mode one-dimensional scan line.

A third process includes subjecting the image of the first process to an intensity-based segmentation process where dark pixels (representing fluid) are automatically separated from bright pixels (representing tissue and other structures).

In a fourth process, the images resulting from the second and third step are combined to result in a single image representing likely amniotic fluid regions.

In a fifth process, the combined image is cleaned to make the output image smooth and to remove extraneous structures such as the fetal head and the fetal bladder.

In a sixth process, boundary line contours are placed on each 2D image. Thereafter, the method then calculates the total 3D volume of amniotic fluid in the uterus.

In cases in which uteruses are too large to fit in a single 3D array of 2D scanplanes or a single 3D scancone of 3D distributed scanlines, especially as occurs during the second and third trimester of pregnancy, preferred alternate embodiments of the invention allow for acquiring at least two 3D data sets, preferably four, each 3D data set having at least a partial ultrasonic view of the uterus, each partial view obtained from a different anatomical site of the patient.

In one embodiment a 3D array of 2D scanplanes is assembled such that the 3D array presents a composite image of the uterus that displays the amniotic fluid regions to provide the basis for calculation of amniotic fluid volumes. In a preferred alternate embodiment, the user acquires the 3D data sets in quarter sections of the uterus when the patient is in a supine position. In this 4-quadrant supine procedure, four image cones of data are acquired near the midpoint of each uterine quadrant at substantially equally spaced intervals between quadrant centers. Image processing as outlined above is conducted for each quadrant image, segmenting on the darker pixels or voxels associated with amniotic fluid. Correcting algorithms are applied to compensate for any quadrant-to-quadrant image cone overlap by registering and fixing one quadrant's image to another. The result is a fixed 3D mosaic image of the uterus and the amniotic fluid volumes or regions in the uterus from the four separate image cones.

Similarly, in another preferred alternate embodiment, the user acquires one or more 3D image data sets of quarter sections of the uterus when the patient is in a lateral position. In this multi-image cone lateral procedure, each image's cones of data are acquired along a lateral line of substantially equally spaced intervals. Each image cone is subjected to the image processing as outlined above, with emphasis given to segmenting on the darker pixels or voxels associated with amniotic fluid. Scanplanes showing common pixel or voxel overlaps are registered into a common coordinate system along the lateral line. Correcting algorithms are applied to compensate for any image cone overlap along the lateral line. The result is a fixed 3D mosaic image of the uterus and the amniotic fluid volumes or regions in the uterus from the four separate image cones.

In yet other preferred embodiments, at least two 3D scancone of 3D distributed scanlines are acquired at different anatomical sites, image processed, registered and fused into a 3D mosaic image composite. Amniotic fluid volumes are then calculated.

The system and method further provides an automatic method to detect and correct for any contribution the fetal head provides to the amniotic fluid volume.

The preferred portable embodiment of the ultrasound transceiver of the amniotic fluid volume measuring system are shown in FIGS. 1-4. The transceiver 10 includes a handle 12 having a trigger 14 and a top button 16, a transceiver housing 18 attached to the handle 12, and a transceiver dome 20. A display 24 for user interaction is attached to the transceiver housing 18 at an end opposite the transceiver dome 20. Housed within the transceiver 10 is a single element transducer (not shown) that converts ultrasound waves to electrical signals. The transceiver 10 is held in position against the body of a patient by a user for image acquisition and signal processing. In operation, the transceiver 10 transmits a radio frequency ultrasound signal at substantially 3.7 MHz to the body and then receives a returning echo signal. To accommodate different patients having a variable range of obesity, the transceiver 10 can be adjusted to transmit a range of probing ultrasound energy from approximately 2 MHz to approximately 10 MHz radio frequencies.

The top button 16 selects for different acquisition volumes. The transceiver is controlled by a microprocessor and software associated with the microprocessor and a digital signal processor of a computer system. As used in this invention, the term "computer system" broadly comprises any microprocessor-based or other computer system capable of executing operating instructions and manipulating data, and is not limited to a traditional desktop or notebook computer. The display 24 presents alphanumeric or graphic data indicating the proper or optimal positioning of the transceiver 10 for initiating a series of scans. The transceiver 10 is configured to initiate the series of scans to obtain and present 3D images as either a 3D array of 2D scanplanes or as a single 3D scancone of 3D distributed scanlines. A suitable transceiver is the DCD372 made by Diagnostic Ultrasound. In alternate embodiments, the two- or three-dimensional image of a scan plane may be presented in the display 24.

Although the preferred ultrasound transceiver is described above, other transceivers may also be used. For example, the transceiver need not be battery-operated or otherwise portable, need not have a top-mounted display 24, and may include many other features or differences. The display 24 may be a liquid crystal display (LCD), a light emitting diode (LED), a cathode ray tube (CRT), or any suitable display capable of presenting alphanumeric data or graphic images.

FIG. 2A is a photograph of the hand-held transceiver 10 for scanning a patient. The transceiver 10 is then positioned over the patient's abdomen by a user holding the handle 12 to place the transceiver housing 18 against the patient's abdomen. The top button 16 is centrally located on the handle 12. Once optimally positioned over the abdomen for scanning, the transceiver 10 transmits an ultrasound signal at substantially 3.7 MHz into the uterus. The transceiver 10 receives a return ultrasound echo signal emanating from the uterus and presents it on the display 24.

FIG. 2B is a perspective view of the hand-held transceiver device sitting in a communication cradle. The transceiver 10 sits in a communication cradle 42 via the handle 12. This cradle can be connected to a standard USB port of any personal computer, enabling all the data on the device to be transferred to the computer and enabling new programs to be transferred into the device from the computer.

FIG. 2C is a perspective view of an amniotic fluid volume measuring system 5A. The system 5A includes the transceiver 10 cradled in the cradle 42 that is in signal communication with a computer 52. The transceiver 10 sits in a communication cradle 42 via the handle 12. This cradle can be connected to a standard USB port of any personal computer 52, enabling all the data on the transceiver 10 to be transferred to the computer for analysis and determination of amniotic fluid volume.

FIG. 3 depicts an alternate embodiment of an amniotic fluid volume measuring system 5B in a schematic view. The system 5B includes a plurality systems 5A in signal communication with a server 56. As illustrated each transceiver 10 is in signal connection with the server 56 through connections via a plurality of computers 52. FIG. 3, by example, depicts each transceiver 10 being used to send probing ultrasound radiation to a uterus of a patient and to subsequently retrieve ultrasound echoes returning from the uterus, convert the ultrasound echoes into digital echo signals, store the digital echo signals, and process the digital echo signals by algorithms of the invention. A user holds the transceiver 10 by the handle 12 to send probing ultrasound signals and to receive incoming ultrasound echoes. The transceiver 10 is placed in the communication cradle 42 that is in signal communication with a computer 52, and operates as an amniotic fluid volume measuring system. Two amniotic fluid volume-measuring systems are depicted as representative though fewer or more systems may be used. As used in this invention, a "server" can be any computer software or hardware that responds to requests or issues commands to or from a client. Likewise, the server may be accessible by one or more client computers via the Internet, or may be in communication over a LAN or other network.

Preferred amniotic fluid volume measuring systems include the transceiver 10 for acquiring data from a patient. The transceiver 10 is placed in the cradle 52 to establish signal communication with the computer 52. Signal communication as illustrated is, in one embodiment, by a wired connection from the cradle 42 to the computer 52. Signal communication between the transceiver 10 and the computer 52 may also be by wireless means, for example, infrared signals or radio frequency signals. The wireless means of signal communication may occur between the cradle 42 and the computer 52, the transceiver 10 and the computer 52, or the transceiver 10 and the cradle 42.

A preferred first embodiment of the amniotic fluid volume measuring system includes each transceiver 10 being separately used on a patient and sending signals proportionate to the received and acquired ultrasound echoes to the computer 52 for storage. Residing in each computer 52 are imaging programs having instructions to prepare and analyze a plurality of one dimensional (1D) images from the stored signals and transforms the plurality of 1D images into the plurality of 2D scanplanes. The imaging programs also present 3D renderings from the plurality of 2D scanplanes. Also residing in each computer 52 are instructions to perform the additional ultrasound image enhancement procedures, including instructions to implement the image processing algorithms.

A preferred second embodiment of the amniotic fluid volume measuring system is similar to the first embodiment, but the imaging programs and the instructions to perform the additional ultrasound enhancement procedures are located on the server 56. Each computer 52 from each amniotic fluid volume measuring system receives the acquired signals from the transceiver 10 via the cradle 51 and stores the signals in the memory of the computer 52. The computer 52 subsequently retrieves the imaging programs and the instructions to perform the additional ultrasound enhancement procedures from the server 56. Thereafter, each computer 52 prepares the 1D images, 2D images, 3D renderings, and enhanced images from the retrieved imaging and ultrasound enhancement procedures. Results from the data analysis procedures are sent to the server 56 for storage.

A preferred third embodiment of the amniotic fluid volume measuring system is similar to the first and second embodiments, but the imaging programs and the instructions to perform the additional ultrasound enhancement procedures are located on the server 56 and executed on the server 56. Each computer 52 from each amniotic fluid volume measuring system receives the acquired signals from the transceiver 10 and via the cradle 51 sends the acquired signals in the memory of the computer 52. The computer 52 subsequently sends the stored signals to the server 56. In the server 56, the imaging programs and the instructions to perform the additional ultrasound enhancement procedures are executed to prepare the 1D images, 2D images, 3D renderings, and enhanced images from the server 56 stored signals. Results from the data analysis procedures are kept on the server 56, or alternatively, sent to the computer 52.

FIG. 4 is another embodiment of an amniotic volume fluid measuring system 5C presented in schematic view. The system 5C includes a plurality of amniotic fluid measuring systems 5A connected to a server 56 over the Internet or other network 64. FIG. 4 represents any of the first, second, or third embodiments of the invention advantageously deployed to other servers and computer systems through connections via the network.

FIG. 5A a graphical representation of a plurality of scan lines forming a single scan plane. FIG. 5A illustrates how ultrasound signals are used to make analyzable images, more specifically how a series of one-dimensional (1D) scanlines are used to produce a two-dimensional (2D) image. The 1D and 2D operational aspects of the single element transducer housed in the transceiver 10 is seen as it rotates mechanically about an angle .phi.. A scanline 214 of length r migrates between a first limiting position 218 and a second limiting position 222 as determined by the value of the angle .phi., creating a fan-like 2D scanplane 210. In one preferred form, the transceiver 10 operates substantially at 3.7 MHz frequency and creates an approximately 18 cm deep scan line 214 and migrates within the angle .phi. having an angle of approximately 0.027 radians. A first motor tilts the transducer approximately 60.degree. clockwise and then counterclockwise forming the fan-like 2D scanplane presenting an approximate 120.degree. 2D sector image. A plurality of scanlines, each scanline substantially equivalent to scanline 214 is recorded, between the first limiting position 218 and the second limiting position 222 formed by the unique tilt angle .phi.. The plurality of scanlines between the two extremes forms a scanplane 210. In the preferred embodiment, each scanplane contains 77 scan lines, although the number of lines can vary within the scope of this invention. The tilt angle .phi. sweeps through angles approximately between -60.degree. and +60.degree. for a total arc of approximately 120.degree..

FIG. 5B is a graphical representation of a plurality of scanplanes forming a three-dimensional array (3D) 240 having a substantially conic shape. FIG. 5B illustrates how a 3D rendering is obtained from the plurality of 2D scanplanes. Within each scanplane 210 are the plurality of scanlines, each scanline equivalent to the scanline 214 and sharing a common rotational angle .theta.. In the preferred embodiment, each scanplane contains 77 scan lines, although the number of lines can vary within the scope of this invention. Each 2D sector image scanplane 210 with tilt angle .phi. and range r (equivalent to the scanline 214) collectively forms a 3D conic array 240 with rotation angle .theta.. After gathering the 2D sector image, a second motor rotates the transducer between 3.75.degree. or 7.5.degree. to gather the next 120.degree. sector image. This process is repeated until the transducer is rotated through 180.degree., resulting in the cone-shaped 3D conic array 240 data set with 24 planes rotationally assembled in the preferred embodiment. The conic array could have fewer or more planes rotationally assembled. For example, preferred alternate embodiments of the conic array could include at least two scanplanes, or a range of scanplanes from 2 to 48 scanplanes. The upper range of the scanplanes can be greater than 48 scanplanes. The tilt angle .phi. indicates the tilt of the scanline from the centerline in 2D sector image, and the rotation angle .theta., identifies the particular rotation plane the sector image lies in. Therefore, any point in this 3D data set can be isolated using coordinates expressed as three parameters, P(r, .phi., .theta.).

As the scanlines are transmitted and received, the returning echoes are interpreted as analog electrical signals by a transducer, converted to digital signals by an analog-to-digital converter, and conveyed to the digital signal processor of the computer system for storage and analysis to determine the locations of the amniotic fluid walls. The computer system is representationally depicted in FIGS. 3 and 4 and includes a microprocessor, random access memory (RAM), or other memory for storing processing instructions and data generated by the transceiver 10.

FIG. 5C is a graphical representation of a plurality of 3D-distributed scanlines emanating from the transceiver 10 forming a scancone 300. The scancone 300 is formed by a plurality of 3D distributed scanlines that comprises a plurality of internal and peripheral scanlines. The scanlines are one-dimensional ultrasound A-lines that emanate from the transceiver 10 at different coordinate directions, that taken as an aggregate, from a conic shape. The 3D-distributed A-lines (scanlines) are not necessarily confined within a scanplane, but instead are directed to sweep throughout the internal and along the periphery of the scancone 300. The 3D-distributed scanlines not only would occupy a given scanplane in a 3D array of 2D scanplanes, but also the inter-scanplane spaces, from the conic axis to and including the conic periphery. The transceiver 10 shows the same illustrated features from FIG. 1, but is configured to distribute the ultrasound A-lines throughout 3D space in different coordinate directions to form the scancone 300.

The internal scanlines are represented by scanlines 312A-C. The number and location of the internal scanlines emanating from the transceiver 10 is the number of internal scanlines needed to be distributed within the scancone 300, at different positional coordinates, to sufficiently visualize structures or images within the scancone 300. The internal scanlines are not peripheral scanlines. The peripheral scanlines are represented by scanlines 314A-F and occupy the conic periphery, thus representing the peripheral limits of the scancone 300.

FIG. 6 is a depiction of the hand-held transceiver placed on a patient trans-abdominally to transmit probing ultrasound and receive ultrasound echoes for processing to determine amniotic fluid volumes. The transceiver 10 is held by the handle 12 to position over a patient to measure the volume of amniotic fluid in an amniotic sac over a baby. A plurality of axes for describing the orientation of the baby, the amniotic sac, and mother is illustrated. The plurality of axes includes a vertical axis depicted on the line L (R)-L(L) for left and right orientations, a horizontal axis LI-LS for inferior and superior orientations, and a depth axis LA-LP for anterior and posterior orientations.

FIG. 6 is representative of a preferred data acquisition protocol used for amniotic fluid volume determination. In this protocol, the transceiver 10 is the hand-held 3D ultrasound device (for example, model DCD372 from Diagnostic Ultrasound) and is used to image the uterus trans-abdominally. Initially during the targeting phase, the patient is in a supine position and the device is operated in a 2D continuous acquisition mode. A 2D continuous mode is where the data is continuously acquired in 2D and presented as a scanplane similar to the scanplane 210 on the display 24 while an operator physically moves the transceiver 10. An operator moves the transceiver 10 around on the maternal abdomen and the presses the trigger 14 of the transceiver 10 and continuously acquires real-time feedback presented in 2D on the display 24. Amniotic fluid, where present, visually appears as dark regions along with an alphanumeric indication of amniotic fluid area (for example, in cm.sup.2) on the display 24. Based on this real-time information in terms of the relative position of the transceiver 10 to the fetus, the operator decides which side of the uterus has more amniotic fluid by the presentation on the display 24. The side having more amniotic fluid presents as regions having larger darker regions on the display 24. Accordingly, the side displaying a large dark region registers greater alphanumeric area while the side with less fluid shows displays smaller dark regions and proportionately registers smaller alphanumeric area on the display 24. While amniotic fluid is present throughout the uterus, its distribution in the uterus depends upon where and how the fetus is positioned within the uterus. There is usually less amniotic fluid around the fetus's spine and back and more amniotic fluid in front of its abdomen and around the limbs.

Based on fetal position information acquired from data gathered under continuous acquisition mode, the patient is placed in a lateral recumbent position such that the fetus is displaced towards the ground creating a large pocket of amniotic fluid close to abdominal surface where the transceiver 10 can be placed as shown in FIG. 6. For example, if large fluid pockets are found on the right side of the patient, the patient is asked to turn with the left side down and if large fluid pockets are found on the left side, the patient is asked to turn with the right side down.

After the patient has been placed in the desired position, the transceiver 10 is again operated in the 2D continuous acquisition mode and is moved around on the lateral surface of the patient's abdomen. The operator finds the location that shows the largest amniotic fluid area based on acquiring the largest dark region imaged and the largest alphanumeric value displayed on the display 24. At the lateral abdominal location providing the largest dark region, the transceiver 10 is held in a fixed position, the trigger 14 is released to acquire a 3D image comprising a set of arrayed scanplanes. The 3D image presents a rotational array of the scanplanes 210 similar to the 3D array 240.

In a preferred alternate data acquisition protocol, the operator can reposition the transceiver 10 to different abdominal locations to acquire new 3D images comprised of different scanplane arrays similar to the 3D array 240. Multiple scan cones obtained from different positions provide the operator the ability to image the entire amniotic fluid region from different view points. In the case of a single image cone being too small to accommodate a large AFV measurement, obtaining multiple 3D array 240 image cones ensures that the total volume of large AFV regions is determined. Multiple 3D images may also be acquired by pressing the top bottom 16 to select multiple conic arrays similar to the 3D array 240.

Depending on the position of the fetus relative to the location of the transceiver 10, a single image scan may present an underestimated volume of AFV due to amniotic fluid pockets that remain hidden behind the limbs of the fetus. The hidden amniotic fluid pockets present as unquantifiable shadow-regions.

To guard against underestimating AFV, repeated positioning the transceiver 10 and rescanning can be done to obtain more than one ultrasound view to maximize detection of amniotic fluid pockets. Repositioning and rescanning provides multiple views as a plurality of the 3D arrays 240 images cones. Acquiring multiple images cones improves the probability of obtaining initial estimates of AFV that otherwise could remain undetected and un-quantified in a single scan.

In an alternative scan protocol, the user determines and scans at only one location on the entire abdomen that shows the maximum amniotic fluid area while the patient is the supine position. As before, when the user presses the top button 16, 2D scanplane images equivalent to the scanplane 210 are continuously acquired and the amniotic fluid area on every image is automatically computed. The user selects one location that shows the maximum amniotic fluid area. At this location, as the user releases the scan button, a full 3D data cone is acquired and stored in the device's memory.

FIG. 7 shows a block diagram overview the image enhancement, segmentation, and polishing algorithms of the amniotic fluid volume measuring system. The enhancement, segmentation, and polishing algorithms are applied to each scanplane 210 or to the entire scan cone 240 to automatically obtain amniotic fluid regions. For scanplanes substantially equivalent to scanplane 210, the algorithms are expressed in two-dimensional terms and use formulas to convert scanplane pixels (picture elements) into area units. For the scan cones substantially equivalent to the 3D conic array 240, the algorithms are expressed in three-dimensional terms and use formulas to convert voxels (volume elements) into volume units.

The algorithms expressed in 2D terms are used during the targeting phase where the operator trans-abdominally positions and repositions the transceiver 10 to obtain real-time feedback about the amniotic fluid area in each scanplane. The algorithms expressed in 3D terms are used to obtain the total amniotic fluid volume computed from the voxels contained within the calculated amniotic fluid regions in the 3D conic array 240.

FIG. 7 represents an overview of a preferred method of the invention and includes a sequence of algorithms, many of which have sub-algorithms described in more specific detail in FIGS. 8A-F. FIG. 7 begins with inputting data of an unprocessed image at step 410. After unprocessed image data 410 is entered (e.g., read from memory, scanned, or otherwise acquired), it is automatically subjected to an image enhancement algorithm 418 that reduces the noise in the data (including speckle noise) using one or more equations while preserving the salient edges on the image using one or more additional equations. Next, the enhanced images are segmented by two different methods whose results are eventually combined. A first segmentation method applies an intensity-based segmentation algorithm 422 that determines all pixels that are potentially fluid pixels based on their intensities. A second segmentation method applies an edge-based segmentation algorithm 438 that relies on detecting the fluid and tissue interfaces. The images obtained by the first segmentation algorithm 422 and the images obtained by the second segmentation algorithm 438 are brought together via a combination algorithm 442 to provide a substantially segmented image. The segmented image obtained from the combination algorithm 442 are then subjected to a polishing algorithm 464 in which the segmented image is cleaned-up by filling gaps with pixels and removing unlikely regions. The image obtained from the polishing algorithm 464 is outputted 480 for calculation of areas and volumes of segmented regions-of-interest. Finally the area or the volume of the segmented region-of-interest is computed 484 by multiplying pixels by a first resolution factor to obtain area, or voxels by a second resolution factor to obtain volume. For example, for pixels having a size of 0.8 mm by 0.8 mm, the first resolution or conversion factor for pixel area is equivalent to 0.64 mm.sup.2, and the second resolution or conversion factor for voxel volume is equivalent to 0.512 mm.sup.3. Different unit lengths for pixels and voxels may be assigned, with a proportional change in pixel area and voxel volume conversion factors.

The enhancement, segmentation and polishing algorithms depicted in FIG. 7 for measuring amniotic fluid areas or volumes are not limited to scanplanes assembled into rotational arrays equivalent to the 3D array 240. As additional examples, the enhancement, segmentation and polishing algorithms depicted in FIG. 7 apply to translation arrays and wedge arrays. Translation arrays are substantially rectilinear image plane slices from incrementally repositioned ultrasound transceivers that are configured to acquire ultrasound rectilinear scanplanes separated by regular or irregular rectilinear spaces. The translation arrays can be made from transceivers configured to advance incrementally, or may be hand-positioned incrementally by an operator. The operator obtains a wedge array from ultrasound transceivers configured to acquire wedge-shaped scanplanes separated by regular or irregular angular spaces, and either mechanistically advanced or hand-tilted incrementally. Any number of scanplanes can be either translationally assembled or wedge-assembled ranges, but preferably in ranges greater than 2 scanplanes.

Other preferred embodiments of the enhancement, segmentation and polishing algorithms depicted in FIG. 7 may be applied to images formed by line arrays, either spiral distributed or reconstructed random-lines. The line arrays are defined using points identified by the coordinates expressed by the three parameters, P(r, .phi., .theta.), where the values or r, .phi., and .theta. can vary.

The enhancement, segmentation and polishing algorithms depicted in FIG. 7 are not limited to ultrasound applications but may be employed in other imaging technologies utilizing scanplane arrays or individual scanplanes. For example, biological-based and non-biological-based images acquired using infrared, visible light, ultraviolet light, microwave, x-ray computed tomography, magnetic resonance, gamma rays, and positron emission are images suitable for the algorithms depicted in FIG. 7. Furthermore, the algorithms depicted in FIG. 7 can be applied to facsimile transmitted images and documents.

FIGS. 8A-E depict expanded details of the preferred embodiments of enhancement, segmentation, and polishing algorithms described in FIG. 7. Each of the following greater detailed algorithms are either implemented on the transceiver 10 itself or are implemented on the host computer 52 or on the server 56 computer to which the ultrasound data is transferred.

FIG. 8A depicts the sub-algorithms of Image Enhancement. The sub-algorithms include a heat filter 514 to reduce noise and a shock filter 518 to sharpen edges. A combination of the heat and shock filters works very well at reducing noise and sharpening the data while preserving the significant discontinuities. First, the noisy signal is filtered using a 1D heat filter (Equation E1 below), which results in the reduction of noise and smoothing of edges. This step is followed by a shock-filtering step 518 (Equation E2 below), which results in the sharpening of the blurred signal. Noise reduction and edge sharpening is achieved by application of the following equations E1-E2. The algorithm of the heat filter 514 uses a heat equation E1. The heat equation E1 in partial differential equation (PDE) form for image processing is expressed as:

.differential..differential..differential..times..differential..differenti- al..times..differential. ##EQU00001##

where u is the image being processed. The image u is 2D, and is comprised of an array of pixels arranged in rows along the x-axis, and an array of pixels arranged in columns along the y-axis. The pixel intensity of each pixel in the image u has an initial input image pixel intensity (I) defined as u.sub.0=I. The value of I depends on the application, and commonly occurs within ranges consistent with the application. For example, I can be as low as 0 to 1, or occupy middle ranges between 0 to 127 or 0 to 512. Similarly, I may have values occupying higher ranges of 0 to 1024 and 0 to 4096, or greater. The heat equation E1 results in a smoothing of the image and is equivalent to the Gaussian filtering of the image. The larger the number of iterations that it is applied for the more the input image is smoothed or blurred and the more the noise that is reduced.

The shock filter 518 is a PDE used to sharpen images as detailed below. The two dimensional shock filter E2 is expressed as:

.differential..differential..function..function..times..gradient. ##EQU00002## where u is the image processed whose initial value is the input image pixel intensity (I): u.sub.0=I where the l(u) term is the Laplacian of the image u, F is a function of the Laplacian, and .parallel..gradient.u.parallel. is the 2D gradient magnitude of image intensity defined by equation E3. .parallel..gradient.u.parallel.= {square root over (u.sub.x.sup.2+u.sub.y.sup.2)}, E3 where u.sub.x.sup.2=the square of the partial derivative of the pixel intensity (u) along the x-axis, u.sub.y.sup.2=the square of the partial derivative of the pixel intensity (u) along the y-axis, the Laplacian l(u) of the image, u, is expressed in equation E4 as l(u)=u.sub.xxu.sub.x.sup.2+2u.sub.xyu.sub.xu.sub.y+u.sub.yyu.sub.y.sup.2 E4 where equation E4 relates to equation E1 as follows: u.sub.x is the first partial derivative

.differential..differential. ##EQU00003## of u along the x-axis, u.sub.y is the first partial derivative

.differential..differential. ##EQU00004## of u along the y-axis, u.sub.x.sup.2 is the square of the first partial derivative

.differential..differential. ##EQU00005## of u along the x-axis, u.sub.y.sup.2 is the square of the first partial derivative

.differential..differential. ##EQU00006## of u along the y-axis, u.sub.xx is the second partial derivative

.differential..times..differential. ##EQU00007## of u along the x-axis, u.sub.yy is the second partial derivative

.differential..times..differential. ##EQU00008## of u along the y-axis, u.sub.xy is cross multiple first partial derivative

.differential..differential. ##EQU00009## of u along the x and y axes, and the sign of the function F modifies the Laplacian by the image gradient values selected to avoid placing spurious edges at points with small gradient values:

.function..function..times..times..times..times..function.>.times..time- s..times..times..gradient.>.times..times..times..times..function.<.t- imes..times..times..times..gradient.>.times..times. ##EQU00010## where t is a threshold on the pixel gradient value .parallel..gradient.u.parallel..

The combination of heat filtering and shock filtering produces an enhanced image ready to undergo the intensity-based and edge-based segmentation algorithms as discussed below.

FIG. 8B depicts the sub-algorithms of Intensity-Based Segmentation (step 422 in FIG. 7). The intensity-based segmentation step 422 uses a "k-means" intensity clustering 522 technique where the enhanced image is subjected to a categorizing "k-means" clustering algorithm. The "k-means" algorithm categorizes pixel intensities into white, gray, and black pixel groups. Given the number of desired clusters or groups of intensities (k), the k-means algorithm is an iterative algorithm comprising fou