**1. Introduction**

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Some of the successful applications of the image processing techniques are in the area of medical imaging. The development of sophisticated imaging devices coupled with the advances in algorithms specific to the medical image processing both for diagnostics and therapeutic planning is the key to the wide popularity of the image processing techniques in the field of medical imaging. Ultrasound imaging is one of the methods of obtaining images from inside the human body through the use of high frequency sound waves. The reflected sound wave echoes are recorded and displayed as a real time visual image. It is a useful way of examining many of the body's internal organs including heart, liver, gall bladder, kidneys and ovaries. The detection of follicles in ultrasound images of ovaries is concerned with the follicle monitoring during the diagnostic process of infertility treatment of patients.

For women undergoing assisted reproductive therapy, the ovarian ultrasound imaging has become an effective tool in infertility management. Among the many causes, ovulatory failure or dysfunction is the main cause for infertility. Thus, an ovary is the most frequently ultrasound scanned organ in an infertile woman. Determination of ovarian status and follicle monitoring constitute the first step in the evaluation of an infertile woman. Infertility can also be associated with the growth of a dominant follicle beyond a preovulatory diameter and subsequent formation of a large anovulatory follicle cyst. The ovary is imaged for its morphology (normal, polycystic or multicystic), for its abnormalities (cysta, dermoids, endometriomas, tumors etc), for its follicular growth in ovulation monitoring, for evidence of ovulation and corpus luteum formation and function. Ovulation scans allow the doctor to determine accurately when the egg matures and when it ovulates. Daily scans are done to visualize the growing follicle, which looks like a black bubble on the screen of the ultrasound imaging machine.

© 2013 Hiremath and Tegnoor; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Hiremath and Tegnoor; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1.1. Ovarian types**

The outcome of ovarian evaluation is the classification of the ovary into one of the three types of ovaries, namely, normal ovary, cystic ovary and polycystic ovary, which are described below :

#### **i.** Normal ovary with antral/dominant follicles

A normal ovary consists of 8-10 follicles from 2mm to 28mm in size [1]. The group of follicles with less than 18mm in size are called antral follicles, and the size in the range of 18-28mm are known as dominant follicles. In a normal menstrual cycle with ovulation, a mature follicle, which is also a cystic structure, develops [2]. The size of a mature follicle that is ready to ovulate is about 18-28mm in diameter. During the past 50 years, it has been accepted that folliculo‐ genesis begins with recruitment of a group or cohort of follicles in the late luteal phase of the preceding menstrual cycle followed by visible follicle growth in the next follicular phase [3]. The group or cohort of follicles begins growth and by the mid-follicular phase, around day 7, a single dominant follicle appears to be selected from the group for accelerated growth [4]. The dominant follicle continues to grow at a rate of about 2mm per day. In women, a preovulatory follicle typically measures 18–28mm when a surge of luteinizing hormone (LH) is released from the pituitary to trigger ovulation; ovulation occurs approximately 36 hours after LH release [5]. The Figure 1 shows an ultrasound image of normal ovary with dominant follicles.

#### **ii.** Ovarian cyst

An ovarian cyst is simply a collection of fluid within the normal solid ovary. There are many different types of ovarian cysts, and they are an extremely common gynaecologic problem. Because of the fear of ovarian cancer, cysts are a common cause of concern among women. But, it is important to know that the vast majority of ovarian cysts are not cancer. However, some benign cysts will require treatment, in that they do not go away by themselves, and in quite rare cases, others may be cancerous [6]. The most common types of ovarian cysts are called functional cysts, which result from a collection of fluid forming around a developing egg. Every woman who is ovulating will form a small amount of fluid around the developing egg each month. The combination of the egg, the special fluid-producing cells, and the fluid is called a follicle and is normally about the size of a pea. For unknown reasons, the cells that surround the egg occasionally form too much fluid, and this straw colored fluid expands the ovary from within. If the collection of fluid gets to be larger than a normal follicle, about threequarters of an inch in diameter, a follicular cyst is said to be present. If fluid continues to be formed, the ovary is stretched as if a balloon was being filled up with water. The covering of the ovary, which is normally white, becomes thin and smooth and appears as a bluish-grey. Rarely, though, follicular cysts may become as large as 3 or 4 inches [7]. The Figure 2 shows an ultrasound image of cystic ovary.

ovarian stroma. The current ultrasound guidelines supported by ESHRE/ASRM consensus characterize the polycystic ovary as containing 12 or more follicles measuring 2–9mm [9]. The basic difference between polycystic and normal ovaries is that, although the polycystic ovaries contain many small antral follicles with eggs in them, the follicles do not develop and mature properly, and hence, there is no ovulation [10]. The infertility incidence with polycystic ovaries is very high. These women usually will have difficulty in getting pregnant and, hence, invariably require treatment to improve chances for pregnancy. In a polycystic ovary, the numerous small cystic structures, also called antral follicles, give the ovaries a characteristic "polycystic" (many cysts) appearance in ultrasound image. It is referred to as polycystic ovarian syndrome (PCOS/PCOD) [11]. Since women with polycystic ovaries do not ovulate regularly, they do not get regular menstrual periods. The Figure 3 shows an ultrasound image of ovary

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A computer assisted diagnostic procedure is desirable because of tedious and time consuming nature of the manual follicle segmentation and ovarian classification done by medical experts. In the present book chapter, the objective of study is to design algorithms for follicle detection and ovarian classification in ultrasound ovarian images in order to assist medical diagnosis in

infertility treatment using digital image processing techniques.

**Figure 1.** Ultrasound image of a normal ovary with dominant follicles

with PCOD.

#### **iii.** Polycystic ovary

The typical polycystic appearance is defined by the presence of 12 or more follicles measuring less than 9mm in diameter arranged peripherally around a dense core of stroma [8]. Other ultrasound features include enlarged ovaries, increased number of follicles and density of

**Figure 1.** Ultrasound image of a normal ovary with dominant follicles

**1.1. Ovarian types**

**ii.** Ovarian cyst

an ultrasound image of cystic ovary.

**iii.** Polycystic ovary

**i.** Normal ovary with antral/dominant follicles

168 Advancements and Breakthroughs in Ultrasound Imaging

below :

The outcome of ovarian evaluation is the classification of the ovary into one of the three types of ovaries, namely, normal ovary, cystic ovary and polycystic ovary, which are described

A normal ovary consists of 8-10 follicles from 2mm to 28mm in size [1]. The group of follicles with less than 18mm in size are called antral follicles, and the size in the range of 18-28mm are known as dominant follicles. In a normal menstrual cycle with ovulation, a mature follicle, which is also a cystic structure, develops [2]. The size of a mature follicle that is ready to ovulate is about 18-28mm in diameter. During the past 50 years, it has been accepted that folliculo‐ genesis begins with recruitment of a group or cohort of follicles in the late luteal phase of the preceding menstrual cycle followed by visible follicle growth in the next follicular phase [3]. The group or cohort of follicles begins growth and by the mid-follicular phase, around day 7, a single dominant follicle appears to be selected from the group for accelerated growth [4]. The dominant follicle continues to grow at a rate of about 2mm per day. In women, a preovulatory follicle typically measures 18–28mm when a surge of luteinizing hormone (LH) is released from the pituitary to trigger ovulation; ovulation occurs approximately 36 hours after LH release [5]. The Figure 1 shows an ultrasound image of normal ovary with dominant follicles.

An ovarian cyst is simply a collection of fluid within the normal solid ovary. There are many different types of ovarian cysts, and they are an extremely common gynaecologic problem. Because of the fear of ovarian cancer, cysts are a common cause of concern among women. But, it is important to know that the vast majority of ovarian cysts are not cancer. However, some benign cysts will require treatment, in that they do not go away by themselves, and in quite rare cases, others may be cancerous [6]. The most common types of ovarian cysts are called functional cysts, which result from a collection of fluid forming around a developing egg. Every woman who is ovulating will form a small amount of fluid around the developing egg each month. The combination of the egg, the special fluid-producing cells, and the fluid is called a follicle and is normally about the size of a pea. For unknown reasons, the cells that surround the egg occasionally form too much fluid, and this straw colored fluid expands the ovary from within. If the collection of fluid gets to be larger than a normal follicle, about threequarters of an inch in diameter, a follicular cyst is said to be present. If fluid continues to be formed, the ovary is stretched as if a balloon was being filled up with water. The covering of the ovary, which is normally white, becomes thin and smooth and appears as a bluish-grey. Rarely, though, follicular cysts may become as large as 3 or 4 inches [7]. The Figure 2 shows

The typical polycystic appearance is defined by the presence of 12 or more follicles measuring less than 9mm in diameter arranged peripherally around a dense core of stroma [8]. Other ultrasound features include enlarged ovaries, increased number of follicles and density of ovarian stroma. The current ultrasound guidelines supported by ESHRE/ASRM consensus characterize the polycystic ovary as containing 12 or more follicles measuring 2–9mm [9]. The basic difference between polycystic and normal ovaries is that, although the polycystic ovaries contain many small antral follicles with eggs in them, the follicles do not develop and mature properly, and hence, there is no ovulation [10]. The infertility incidence with polycystic ovaries is very high. These women usually will have difficulty in getting pregnant and, hence, invariably require treatment to improve chances for pregnancy. In a polycystic ovary, the numerous small cystic structures, also called antral follicles, give the ovaries a characteristic "polycystic" (many cysts) appearance in ultrasound image. It is referred to as polycystic ovarian syndrome (PCOS/PCOD) [11]. Since women with polycystic ovaries do not ovulate regularly, they do not get regular menstrual periods. The Figure 3 shows an ultrasound image of ovary with PCOD.

A computer assisted diagnostic procedure is desirable because of tedious and time consuming nature of the manual follicle segmentation and ovarian classification done by medical experts. In the present book chapter, the objective of study is to design algorithms for follicle detection and ovarian classification in ultrasound ovarian images in order to assist medical diagnosis in infertility treatment using digital image processing techniques.

**Figure 2.** Ultrasound image of a cystic ovary

## **1.2. Background literature**

In [12,13], follicular ultrasound images are segmented using an optimal thresholding method applied to coarsely estimated ovary. However, this fully automated method, using the edges for estimation of ovary boundaries and thresholding as a segmentation method, doesn't give optimal results. In [14], this method has been upgraded using active contours and, conse‐ quently, the segmentation quality of recognized follicles is considerably improved. Edges of recognized objects were much closer to the real follicle boundaries. However, the determina‐ tion of suitable parameters for snakes automatically is problematic. In [15], region growing based segmentation method is used for the follicle detection. In [16], a semi automated method is proposed for the outer follicle wall segmentation, wherein a frequent manual tracing of the inner border of all follicles is done. In [17], cellular automata and cellular neural networks are employed for the follicle segmentation. The results are found to be very promising but an obvious drawback of these two methods is the difficulty in determination of the required parameters for follicle segmentation. In [18], the authors have determined the inner border of all follicles using watershed segmentation techniques. Watershed segmentation was applied on smoothed image data which merged some small adjacent follicles. Therefore binary mathematical morphology was employed to separate such areas adhoc in some steps. In [19], the authors have reported follicle segmentation based on modified region growing method for the follicle segmentation and linear discriminant classifier for the purpose of classification. In [20], the scanline thresholding method is used for the follicle detection and the segmentation

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The follicles are the regions of interest (ROIs) in an ovarian ultrasound image, which need to be detected by using image processing techniques. This is basically an object recognition problem. Thus, the basic image processing steps, namely, preprocessing, segmentation, feature extraction and classification, apply. In the literature, the authors have employed various

performance is measured in terms of the mean square error (MSE).

techniques for ultrasound image processing, as shown below:

– Homogeneous region growing mean filter (HRGMF)

**1.** Preprocessing

**2.** Segmentation

– Gaussian low pass filter

**Figure 3.** Ultrasound image of an ovary with PCOD

– Contourlet transform

– Optimal thresholding – Edge based method

Follicle Detection and Ovarian Classification in Digital Ultrasound Images of Ovaries http://dx.doi.org/10.5772/56518 171

**Figure 3.** Ultrasound image of an ovary with PCOD

the authors have reported follicle segmentation based on modified region growing method for the follicle segmentation and linear discriminant classifier for the purpose of classification. In [20], the scanline thresholding method is used for the follicle detection and the segmentation performance is measured in terms of the mean square error (MSE).

The follicles are the regions of interest (ROIs) in an ovarian ultrasound image, which need to be detected by using image processing techniques. This is basically an object recognition problem. Thus, the basic image processing steps, namely, preprocessing, segmentation, feature extraction and classification, apply. In the literature, the authors have employed various techniques for ultrasound image processing, as shown below:

**1.** Preprocessing

**1.2. Background literature**

**Figure 2.** Ultrasound image of a cystic ovary

170 Advancements and Breakthroughs in Ultrasound Imaging

In [12,13], follicular ultrasound images are segmented using an optimal thresholding method applied to coarsely estimated ovary. However, this fully automated method, using the edges for estimation of ovary boundaries and thresholding as a segmentation method, doesn't give optimal results. In [14], this method has been upgraded using active contours and, conse‐ quently, the segmentation quality of recognized follicles is considerably improved. Edges of recognized objects were much closer to the real follicle boundaries. However, the determina‐ tion of suitable parameters for snakes automatically is problematic. In [15], region growing based segmentation method is used for the follicle detection. In [16], a semi automated method is proposed for the outer follicle wall segmentation, wherein a frequent manual tracing of the inner border of all follicles is done. In [17], cellular automata and cellular neural networks are employed for the follicle segmentation. The results are found to be very promising but an obvious drawback of these two methods is the difficulty in determination of the required parameters for follicle segmentation. In [18], the authors have determined the inner border of all follicles using watershed segmentation techniques. Watershed segmentation was applied on smoothed image data which merged some small adjacent follicles. Therefore binary mathematical morphology was employed to separate such areas adhoc in some steps. In [19],

	- Optimal thresholding
	- Edge based method
	- Geometric features
	- Texture features
	- 3σ interval based classifier.
	- K-NN classifier
	- Linear discriminant classifier
	- Fuzzy classifier
	- SVM classifier

#### **1.3. Image data set**

For the purpose of experimentation, the databases D1, D2 and D3, of ultrasound images of ovaries are prepared in consultation with the medical expert, namely, Radiologist and Gyneocologist, for the present study of the follicle detection and ovarian classification in ovarian images. Some of the images are captured by the Toshiba [Model SSA-320A/325A] diagnostic ultrasound system with the transvaginal transducer frequency 26 Hz. Some of the images are obtained from the publicly available websites [www.radiologyinfo.com; www.ovaryresearch.com]. The image dataset D1 consists of the 80 ultrasound ovarian images, with the size 256x256. The image dataset D2 consists of the 90 ultrasound ovarian images, with the size 512x512. The image dataset D3 consists of the 70 ultrasound ovarian images, with the size 512x512. It contains the images of 30 normal (healthy) ovaries, 20 polycystic ovaries and 20 cystic ovaries.

are compared with the inferences drawn by medical expert. Initially, the image is processed with Gaussian low pass filter yielding denoised image. Then canny operator is applied to detect the edges from the denoised image. Morphological dilation is performed by using the disk shaped structuring element with radius 1 to fill the weak edges. which yields the segmented image. Possible holes inside the segmented regions are filled. Any spurious regions due to noise are eliminated by morphological erosion. The regions in segmented image having smaller area than the threshold T (empirical value) are removed. The segmented regions are labeled. Set all the nonzero pixels of the border of segmented image to zero, which yields the

Follicle Detection and Ovarian Classification in Digital Ultrasound Images of Ovaries

final segmented image. Thus the plausible follicle regions are the labeled segments.

<sup>B</sup><sup>2</sup> <sup>∑</sup> i,j=1 B

the extent E and the centriod Y= <sup>1</sup>

into follicles and non-follicles. The *<sup>R</sup>*¯, *<sup>C</sup>*¯

parameters R, Cp, Cr, Tr, E and C=(Cx,Cy).

The geometric features are extracted for the known follicle regions. The main aim of geometric feature extraction is to recognize geometric properties of ovarian follicles in ultrasound images. The ovarian follicles are oval shaped compact structures, which resemble the circular/ellipse and are, thus, characterized by the seven geometric features, namely, the area A, the ratio R of majoraxislength to minoraxislength, the compactness Cp, the circularity Cr, the tortousity Tr,

The classification stage comprises two steps, namely, the training phase and the testing phase.

*Training phase* : In the training phase, the geometrical features, are computed for regions known to be follicles in the training images in consultation with the medical expert. Then, the sample means and standard deviations of the geometric parameters R, Cp, Cr, Tr, E and, *C* =(*Cx*, *Cy*) are computed which are used to set the rules for classification of labelled segments

the *σR*, *σCp*, *σCr*, *σTr*, *σE*, (*σCx*, *σCy*) denote the standard deviation values, of the geometric

Now, the classification rules for follicle recognition are formulated as following: A region with area A, ratio R, compactness Cp, circularity Cr, Tortousity Tr, extent E, and centriod

*p*, *C*¯ *r*, *T*¯

*C* =(*Cx*, *Cy*), is classified as a follicle, if the following conditions are satisfied:

s

s

s

*R RR R R* - a < < + a

*Cp Cp Cp Cp Cp* -a < < +a

*Cr Cr Cr Cr Cr* -a < < +a

 s

> s

 s

CS-i,-j(F-1(λ(F(CSi,j(X))))) .

*<sup>r</sup>*, *<sup>E</sup>*¯, (*<sup>C</sup>*¯

*x*, *C*¯

A>TA (1)

(2)

(3)

(4)

*y*) denote the mean values, and,

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