Automated Algorithm for Ovarian Cysts Detection in ...

2013 2nd International Conference on Advances in Biomedical Engineering

Automated Algorithm for Ovarian Cysts Detection in Ultrasonogram Sandy RIHANA(1) (1)

Hares Moussallem(2) (2)

Biomedical Engineering Department

Chiraz Skaf(2)

Charles Yaacoub(2)

Telecommunications Engineering Department

Holy Spirit University of Kaslik (USEK) Jounieh, Lebanon [email protected]

contrast enhancement [4], and horizontal and vertical thresholding for cysts segmentation [5] .

Abstract— Polycystic Ovary Syndrome (PCOS) is a female endocrine disorder which severely affects women’s health and its diagnostic requires medical treatment or even surgery. Manual analysis of PCOS diagnosis often produces errors. Recently, many automated algorithms have been studied for polycysts detection in Ultrasound images. This paper presents cysts detection and classification in the ovary ultrasound images with an accuracy that reaches 90%. Keywords—ultrasound medical imaging, thresholding, multiscale morphological method , svm

The method presented in this paper consists of applying a multi-scale morphological process for noise reduction and for the contrast enhancement, followed by segmentation and detection. It aims at extracting features that every clinician bases his diagnostic on. These parameters are major axis, minor axis, area and perimeter. In addition, it allows us to differentiate between simple, polycysts and endometrioma cysts, by calculating the mean and the standard deviation of a sub-image extracted from each detected cyst.

cysts,

I. INTRODUCTION Diagnostic ultrasound (US) is nowadays the most common noninvasive medical-imaging modality. In fact, the first step in the roadmap for the diagnostic of ovarian cystic masses is based on ovarian ultrasound. Ovarian ultrasounds are maneuvered by gynecologist in order to detect and heal cysts that may occur. These cysts are developed due to incomplete developed follicles in the ovaries. They can generally be detected on the ultrasound images by some dark regions, darker than other regions in the same image, thus tracing a sort of edge of an elliptic geometric shape. Manual analysis by clinicians is generally used in the diagnostic. Generally, periodic measurements of the size, texture and shape of follicles over several days are the primary means of evaluation. Nevertheless, nowadays, automated software able to help the clinicians to identify the cysts and to reduce the burden of the clinical diagnosis in order to differentiate among malignant and benign cysts could be appropriate. The objective of this paper is to develop an extended processing scheme for automatic detection of follicles in ultrasound images of ovaries. Different methods have been developed in the literature on the identification of the ovarian cysts. Potocnik and Zazula worked on a method based on optimal thresholding [1] and then upgraded by using active contour technique [2] for the segmentation of follicles. Cigale and Zazula implemented the neural network approach for the segmentation [3]. Others use the multiscale morphological method for the denoising,

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II. MATERIALS AND METHODS US image acquisition Digital recordings of ovaries ultrasound scans have been provided by the obstetrics gynecology polyclinic MD. Barakat. These images were performed on a group of women with no cysts, simple cysts and polycysts syndrome. Some of the simple cysts syndromes present endometrioma cyst [1]. These images were assessed by the clinician through periodic examinations and some of them through surgery. At total, 25 ultrasound images are used for the development of this method. US image processing Figure 1 shows the image processing flowchart developed in this paper. A preprocessing part consists of contrast enhancement of the grayscale image and in image binarization. A post processing part consists of detecting and labelling connected components leading finally to geometrical features extraction and classification of the cysts. A. Image preprocessing After converting the images into grayscale, contrast enhancement is performed based on morphological operation, such as top hat and bottom hat. The former top hat, returns the image minus the morphological opening of the image (erosion followed by dilation) while the bottom hat transformation returns the image minus the morphological closing of the image (dilation followed by erosion). Equation 1 summarizes the process:

m

A threshold T2 = K2σr is applied to the image for the binarization, where K2 is a scale multiplication factor. Same procedure is applied for each column as expressed in equations (4) and (5), where K4 is the scaling factor and T4 = K4σr.

m

g% ( r , c ) = g ( r , c ) + 0.5∑ FiST ( r , c ) − 0.5∑ FiSB (r , c ), i =1

(1)

i =1

where ğ(r , c) the output pixel at coordinates (r , c) , S is a disk-

mc =

σc =

1 M

1 M

M

∑ g (r, c )

(4)

r =1

M

∑ ( g ( r, c ) − m )

2

c

(5)

r =1

The results of horizontal and vertical scan-line thresholding are then merged by applying the logical “OR” operation to yield at the end a binary image mask. B. Image postprocessing Applying morphological opening on the binary image enhances the quality of the image obtained by removing the undesired small components. The morphological opening operation is an erosion followed by a dilation, using the same structuring element for both operations. The structuring element used is disk-shaped element with radius R=3.

Figure 1- General bloc diagram

shaped structuring element of radius R=3 empirically used for morphological opening and closing, FTiS and FBiS the top hat and bottom hat transforms at scale i, containg respectively bright and dark features smaller than S, and g the input image. In a medical ultrasound image many undesired structures blur the desired outcome of the image like blood vessels, nerve fibers, lymphatic glands and added noise due to the ultrasound waves propagation. So the detection of cysts becomes a challenging task in such a noisy image. Therefore, traditional edge based techniques (such as sobel, prewitt) give false results when applied on these images due to added noise. The follicle appears as a homogenous region in the ultrasound image. The gray level values for the pixel within the follicles will be more or less the same as the background. The thresholding method proposed is based on horizontal and vertical scanline thresholding respectively then merging both results in order to obtain the binary image. The horizontal scanline thresholding could be described as follows. The image g of size M x N is considered. The mean mr and standard deviation σr of the rth row sub image are given by equations (2) and (3), respectively.

mr = σr =

1 N

1 N

N

∑ g ( r, c )

(2)

c =1

N

∑ ( g (r, c ) − m ) r

2

(3)

c =1

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The region of interest (ROI) window is covered by waves emitted by the US probe. The use of a mask having the same shape as seen in Figure 3 limits the ROI and helps reducing false detection. The mask is generated with adobe Photoshop CS version by assigning to the ROI area a white color (i.e. logic ‘1’) and the affecting the remaining area with black (i.e. logic ‘0’). The binary image mask obtained after morphological opening and the mask specifying the ROI are then multiplied in order to better detect connected components and filter out undesired image areas.

Figure 2- ROI Mask of same size of the original image representing the angle of the probe

Generally, two adjacent pixels, assigned with similar values belong to the same component. 8-connectity is used to elaborate all connected components in the image. Connected components within a certain range of size assumed to be probable follicles or cysts are considered in the next steps. C. Feature extraction The medical diagnosis for identifying the cyst is based on indicators such as the number of follicles exceeding a certain size and their relative position in the ovary. Measurement is mandatory and periodical, even daily analysis could be performed over 8-10 days, depending on the situation. To efficiently characterize follicles, some parameters should be known to be compared with standard parameters. Geometrical and texture features of the ovarian cysts in ultrasound images such as area, major axis length, minor axis length, major axis length to minor axis length ration, compactness extent, centroid

and so on helps characterizing these follicles. Following the clinical flowchart of the clinician, the Area, the Major axis length, the Minor axis length, and the centroid are extracted as geometrical features. Area is the number of pixels included inside the potential follicle. A circular form at a diameter normally between 2 mm and 30 mm gives an area of 4mm2 to 700mm2. This factor is crucial in differentiating between a follicle and a cyst. Using the resolution (DPI) of the ultrasound machine, the area in the metric system is calculated. Knowing the major axis and the minor axis, the area is computed (6). Area = π • (Major Axis • Minor Axis)

(6)

Major and minor axis: all follicles and cysts have an oval shape close to an elliptic form, and are therefore modeled by an ellipse. The major axis and the minor axis are the ones corresponding to the ellipse having the same 2nd central moment as the segmented area (follicle or cyst). The centroid is the center of mass of the region of interest characterized by its x and y coordinates. Objects of interest in an ultrasound are better visualized when they are in the middle of the field of view (angle of the probe). Geometrical features extraction orients the diagnosis toward the absence or the presence of cysts based on its location, shape, area… Nevertheless, it does not give a clue about the type of the cyst, whether it is an endometrioma cyst or a normal one. This differentiation will be done using texturebased feature extraction. Figure 3 illustrates the difference in texture between a normal cyst and an endometrioma cyst.

interference. To validate the algorithm cited above, the “Receiver Operating Characteristic” (ROC) analysis is a common means of comparing precision, accuracy, and efficiency. This method shows a good evaluation with simple and clear criteria, used by the “American Statistical Association” in the medical field. A population has been chosen divided into 4 parts; True negative (TN), false negative (FN), false positive (FP) and true positive (TP). There are two potentials of errors: FP and FN; either the individual is nondiseased with positive test or diseased with negative test. TP is for diseased individual with positive test and TN is for nondiseased individual with negative test. 80 images were divided into 4 groups equally, between simple, poly, and endometrioma cysts in addition to 20 healthy non-containing cysts. The accuracy is defined as the capability of giving the right choice without distinguishing between positive and negative. Accuracy =

TP + TN × 100% TP + FP + TN + FN

(7)

The sensitivity is defined as the proportion of patients having a disease and detected by the algorithm. Sensitivity =

TP × 100% TP + FN

(8)

The specificity is defined as the portion of people diagnosted free from any disease and in the same time they are not ill. TN Specificity = × 100% (9) TN + FP Figure 5 shows the importance of top hat and bottom hat filter transformations and how the contrast of the dark regions is enhanced. Comparing to Figure 4, the edge of the cysts is getting more evident.

Figure 3- Normal (left) and Endometrioma (right) cysts

To be able to differentiate between these two types of cysts, a sub-image of 17x17 is extracted from each identified region, centered at the centroid, thus getting sure that the processing is inside the ROI, then the mean and the standard deviation are computed for each type of cyst. These two parameters are different for dissimilar types and provide a reliable parameter to classify the two types of cysts based on the texture. D. Classification and validation using ROC Linear Separate vector machine is used as a classification method. The classifier takes the mean and the standard deviation as input vector and gives an output differentiating between normal and endometria cysts. . SVM is easily implemented comparing to “Neural Network” or other classifiers: Phase1- training: Inputs to the SVM, 2 vectors; an input training feature vector and a class vector as the output corresponding to the input. Phase 2- testing: a test vector as input to validate the SVM classifier. This classifier is usually reliable in differentiating between only 2 classes having no

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We consider next another ultrasonogram and show the results of the binarization process. Figure 6 shows the result of vertical scanline thresholding, and Figure 7 the horizontal scanline. Both images are merged in Figure 8 using the logical “OR” operator. The next step is to find the connected components based on 8-connectivity; Figure 10 shows the connected components detected and labeled. The only cyst is labeled 1 and the other connected components were labeled as 2, 3, 4, 5 and 6 on the black background. These small groups of white pixels should be removed in order to decrease the probability of false cysts detections. After applying the constraint on the cyst’s size and location based on the area, centroid and location parameters, Figure 1 and 12 illustrate the resulting segmented ultrasonogram of a simple cyst, and polycyst (bi-cyst) US respectively. For texture based classification, the mean and the standard deviation of the segmented cysts characterize the image. Twenty simple cysts images and twenty endometrioma cysts were taken for training phase. Another 5 images of each type were used for the testing phase. SVM classifier is used and the Figure represents the classified cysts.

Figure 4-Grayscale ultrasonogram

Figure 5 - After contrast enhancement Figure 11- Connected components found and labeled

Figure 6-Horizontal scanline and Vertical scanline thresholding

Figure 12- Simple cyst segmented

Figure 7- Vertical and Horizontal merged the logical OR operator

As we see the simple cyst class is found above the kernel function and the endometrioma are below the kernel function. To validate this algorithm, the accuracy, sensitivity and specificity have been computed : accuracy= 90% , sensitivity= 88.33%, specificity= 95%

Figure 13- Bi-cyst segmented and labeled

Figure 14- Graph representing simple and endometrioma classes separated by the linear kernel function

ACKNOWLEDGMENT Figure 10- (a) Result of morphological opening, (b) ROI mask, (c) negative of the result obtained by multiplying image in (a) with the one in (b).

The authors would like to address their special thanks to MD. Habib Barakat, who provided the ultrasonograms and the clinical assessment of each ultrasound image.

IV. CONCLUSION

REFERENCES

This paper presents the elaboration of a new algorithm capable of detecting the cycts in ovarian ultrasound images and of differentiating between the two types of cysts. This detection and classification has been made based on the geometrical features of the cysts their texture. The accuracy found of 90% is a promising result. For future work, the algorithm would be improved to generalize the work on all ovarian cyst types, including Dermoid cysts, while keeping in mind the improvement of the classification accuracy. Increasing the database size and the number of images would therefore be necessary for a better evaluation of the solution.

[1] A.H.Balen, J.S.E.Laven, S-L.Tan, D.Dewailly, "Ultrasound assessment of the polycystic ovary”, international consensus definitions, Hum Reprod Update 2003; 9:505-514. [2] Cigale B and Zazula D, "Segmentation of ovarian ultrasound images using cellular neural networks," in Proc. 7th Inter .work. systems, signals and image processing, 2000. [3] Anthony Krivanek and Milan Sonka, "Ovarian Ultrasound Image Analysis: Follicle Segmentation," IEEE Transaction on medical imaging, vol. 17, pp. 935-944, 1998. [4] P.S.Hiremath and Prema Akkasaliger, "Despeckling medical ultrasound images using the contourlet transform," in 4th AMS Indian International Conference on Artificial Intelligence, 2009. [5] P.S.Hiremath and J.R.Tegnoor, "Automated detection of follicle in ultrasound images of ovaries using edge based method," Recent trends in image processing and pattern recognition, pp. 120-125, 2010.

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