These spurious regions must be removed as much as possible. Therefore, the regions having an area less than T empirical value are removed Figure 5 f.
The Figure 5 g depicts the segmented follicles outlined in white superimposed on the original image. The Figure 5 h depicts the recognized follicles after applying the classification rules and the Figure 5 i shows the follicles annotated manually by the medical expert.
Original ultrasound image of the ovary and resultant images at different steps of proposed method. Comparison of resultant images of the active contour method with the HVST based method for two different original images. The Figure 6 depicts comparison of the active contour method with the HVST based segmentation method.
In the Figure 6 , a and e are two original images, while b and f are their corresponding segmented images by HVST method. Similarly, c and g are segmented images of active contour method. It is observed that the follicles, which were not detected by HVST based segmentation method Figure 6 b and f , are correctly identified by the active contour method Figure 6 c and g. The Figure 6 d and h show the manual segmentation rendered by the medical expert.
Hence, the classification accuracy is improved in the active contour method as compared to HVST based segmentation method. The Table 3 presents the comparison of the experimental results of active contour method and HVST based method obtained for the original images in Figure 6.
Further, the active contour method is found to yield more accurate results. Comparison of experimental results of active contour method and HVST based method for original images in Figure 6.
The Table 4 shows the comparison of classification results of the active contour method and the HVST based method after performing ten-fold experiments for both the data sets D1 and D2.
The average detection rates for the active contour method using D1 and D2 sets are Clearly, the method based on active contours outperforms the HVST based method. Comparison of average classification results of the active contour method with the HVST based method after performing ten-fold experiments.
To improve the performance of follicle detection in ultrasound images of ovaries, a new algorithm using fuzzy logic is developed. The method employs contourlet transform for despeckling, histogram equalization and negative transformation in the preprocessing step, the active contours without edges method for segmentation and fuzzy logic for classification.
The output of the fuzzy logic block is a follicle class or non follicle class. The fuzzy-knowledge-base consists of a set of physically interpretable if-then rules providing physical insight into the process. The experimental results demonstrate the efficacy of the fuzzy logic based method [ 40 ]. Fuzzy set theory has been successfully applied to many fields, such as pattern recognition, control systems, and medical applications [ 41 , 42 ].
It has also been effectively used to develop various techniques in image processing tasks including ultrasound images [ 43 ]. Fuzzy logic was initiated in by L. Zadeh [ 45 - 48 ]. The Gaussian membership function is used for each of the fuzzy input variables with mean and standard deviation of the corresponding variables. Let mfr1, mfcp1, mfcr1, mftr1, mfe1, mfcx1 and mfcy1 be the Gaussian membership functions of these input variables belonging to the non-follicle class.
The mean and standard deviation of these parametric values are stored as knowledge base which is shown in the Table 5.
These values are used as the pattern parameters in FIS membership function design. The knowledge base of mean and standard deviation of parametric values of geometric features or the follicle regions.
The construction of the membership functions for the output variables is done in the similar manner. Since this is Sugeno-type inference precisely, zero-order Sugeno , constant type of output variable fits the best to the given set of outputs 1 for follicle and 0 to non-follicle classes. The fuzzy rules for classification procedure in verbose format are as follows:. At this point, the fuzzy inference system has been completely defined, in that the variables, membership functions and the rules necessary to determine the output classes are in place.
Experimental results: The ten-fold experiments are performed for the classification and the average follicle detection rate is computed. The Figure 7 a depicts an original ultrasound image of the ovary and the resultant images at different steps of fuzzy based method are shown in Figure 7 b - e. Therefore, the regions having an area less than T are removed Figure 7 f.
The Figure 7 g depicts the segmented follicles outlined in white superimposed on the original image. The Figure 7 h depicts the recognized follicles after applying classification rules and Figure 7 i shows the follicles annotated manually by the medical expert.
It is observed that the regions which are misclassified as follicles, by the method in [section 3 i ] Figure 8 b and e , are correctly classified by the fuzzy logic based method Figure 8 c and f.
Hence, the classification accuracy of the fuzzy logic based method is improved as compared to the method in [section 3 i ]. The Table 6 presents number of follicles detected in the results of the fuzzy based method and the method in [section 3 i ] corresponding to the original images in the Figure 8. The Table 7 shows the comparison of classification results of fuzzy based method and the method in [section 3 i ] after ten-fold experiments for both the data sets. The average detection rates for the fuzzy based method with D1 and D2 sets are The average detection rates for the method in the Section 3 i with D1 and D2 sets are Original ultrasound image of the ovary and resultant images at different steps of fuzzy based method.
The ovaries are classified into three types based on the number and size of the follicles. Ovary is scanned, follicles are identified by using the method described the section 3, and the size of the follicles are measured and the number of follicles are counted. The ovary containing follicles with size measuring greater than 28mm in size, is a cystic ovary. The ovary containing 12 or more follicles with size measuring less than 10mm, is a polycystic ovary.
The ovary containing follicles with size measuring mm, are antral follicles and with the mm size, are dominant follicles, is a normal ovary with m number of antral follicles and n number of dominant follicles. The two ovarian classification methods are discussed below [Hiremath and Tegnoor, ]:. Training phase: The fuzzy inference system FIS of sugeno type is employed using the fuzzy input variables the number of follicles NN and the size of the follicle S and output variables: normal, cystic and polycystic ovarian classes.
The Trapezoidal membership function is used for each of the fuzzy input variables with minimum and maximum value of the corresponding variables. Let mfn1 and mfs1, mfn2 and mfs2, and mfn3 and mfs3, be the trapezoidal membership functions of the fuzzy input variables NN and S respectively, belonging to the normal ovarian class, cystic ovarian class, polycystic ovarian class.
During the training phase, the parameters, namely, NN and S, are computed for the ovarian images known to be healthy, cystic and polycystic ovary in the training images in consultation with the medical expert, which are used to set the rules for classification of ovarian images. The number of follicles and size of follicles of these three classes are stored as knowledge base which is shown in the Table 8. Since this is Sugeno-type inference precisely, zero-order Sugeno , constant type of output variable fits the best to the given set of outputs 0 for normal ovary and 0.
Based on the descriptions of the inputs the number of the follicles NN and size of the follicles S and output variables normal ovary, cystic ovary, polycystic ovary. Testing phase: During the testing phase, we compute the number of follicles NN, and the size of each follicle S, for an ovary with the detected follicles and apply the above classification rules to determine whether an ovary is normal, cystic, and polycystic. Experimental results : The experimentation is done using image data set D3.
The D3 set consists of 70 sample ultrasound ovarian images of size x, out of which 35 images are used for training and 35 for testing. In the first step, the ten-fold experiments for the follicle detection are done and then the average follicle detection rate is computed.
Further, in the second step, for the ovary classification, 70 sample images of the detected follicles are used, of which 35 are used for training and 35 are used for testing. The ten-fold experiments for the ovarian classification are done and the average classification rate for the ovarian type is computed. The Figure 9 shows the sample results for the ovarian classification method.
The Table 9 shows the average classification results of follicle detection method after performing ten-fold experiments for the image data set. The follicle detection method Section 3 ii yields the average detection rate for the fuzzy based method The Table 10 shows the ten-fold experimental results of the ovarian classification based on fuzzy inference rules. Sample results for proposed ovarian classification method. The aim of support vector machine SVM is to devise a computationally efficient way of learning separating hyper planes in a high dimensional feature space [ 49 ].
The SVMs have been shown to be an efficient method for many real-world problems because of its high generalization performance without the need to add a priori knowledge. Thus, SVMs have much attention as a successful tool for classification [ 50 , 51 ], image recognition [ 52 , 53 ] and bioinformatics [ 54 ]. The SVM model can map the input vectors into a high-dimensional feature space through some non-linear mapping, chosen a priori.
In this space, an optimal separating hyperplane is constructed. SVM is the implementation of the structural risk minimization principle whose object is to minimize the upper bound on the generalization error.
Given a set of training vectors l in total belonging to separate classes, x1, y1 , x2, y2 , x3, y3 , In the possible hyperplanes, only one maximizes the margin and the nearest data point of each class. The Figure 10 shows the optimal separating hyperplane with the largest margin. The support vectors denote the points lying on the margin border. The solution to the classification is given by the decision function in the equation The radial kernels perform best in our experimental comparison, and, hence, are chosen in the proposed diagnosis system.
The radial kernels are defined as equation By using the SVM method, firstly, follicles are detected and secondly, the ovarian classification is performed [ 55 ]. During the training phase, the parameters, namely, the number of follicles NN and the size of the follicle S, are determined for the ovarian images known to be normal, cystic and polycystic ovary in the training images in consultation with the medical expert. The quadratic kernel is used for training the three-class SVM classifier; normal, cystic and polycystic ovary being the three classes.
Optimal hyperplane for support vector machine. During the testing phase, the parameters, namely, the number of follicles NN and the size of follicles S, are determined, and then, the SVM classifier is used to determine whether an ovary is normal, cystic, or polycystic.
Experimental results : The Table 11 shows the comparison of classification results of SVM based method and the fuzzy based method in [section 4 i ] after ten-fold experiments. The average follicle detection rate for the SVM method is The average detection rate for the method in [section 4 i ] is It is observed that the classification accuracy is improved in the SVM based method as compared to the fuzzy based method [section 4 i ]. Comparison of classification results of SVM based method and fuzzy based method [section 4 i ] for follicle detection after ten-fold experiments.
The Table 12 shows the ten-fold experimental results of the ovarian classification based on SVM. From maintaining a sound health of the reproductive system to escalating the chances of healthy pregnancy, ovaries play a crucial role. Undoubtedly, the importance of ovaries is widely acclaimed; but do you know the ovary size also contributes to increasing the chances of pregnancy?
It actually does! Vardaan Medical Centre is an esteemed facility known for the best gynecology treatment and consultation. Our fertility treatment follows the global service standard, and that is the reason, we always succeed to meet expectations of our patients. If you are also struggling with any kind of fertility concerns, look no further but Vardaan Medical Centre is the destination to find a solution with the desired outcomes.
It is the ovaries where eggs are produced. For a smooth and easy pregnancy, the ovary size matters a lot as small sized ovaries implies a lower egg reserve for a woman. Though it does not mean that a large size ovary would ensure egg reserve to be high for women. The size of ovaries are changeable that depends on various factors which are as below —. In a normal lifespan of a woman, ovary size can change many times for multiple reasons.
Age, pregnancy, ovarian disorders, and hormonal stimulation are few of the well-established factors causing change in ovary size. Vardaan Medical Centre highlights the role and importance of ovary size and helps women who are trying to conceive with the required information. It is evident that ovary size influences the good conceiving ability of a woman. The ovaries begin to develop when female embryos are around 8 weeks old and during pregnancy they undergo a number of changes that prepare them for their role in reproduction when a woman is in her childbearing years.
A young woman with small ovaries has a greater chance of having difficulties achieving a normal full-term pregnancy. This is because she will have a lower egg reserve. Having large ovaries does not mean that a woman is more fertile.
For example, ovaries can be large in size as a result of cysts or tumours. They are more commonly large in size in women with so-called polycystic ovaries. Ovaries can change in size due to several factors, which we will discuss below. Here are the various factors that can cause a change in the size of ovaries:. Ovarian size changes with age. Before puberty or after menopause, they measure less than 20 mm in diameter. Ovaries also become larger when a woman is ovulating or menstruating.
Ovarian disorders and cancer can cause an increase in the size of the ovaries. Conditions such as polycystic ovarian syndrome PCOS , follicular cysts, and corpus luteum cysts cause the ovaries to become enlarged, resulting in pain and internal bleeding.
These disorders make it problematic for women to get pregnant. Women who have been diagnosed with infertility often undergo fertility treatments to get pregnant. A part of these treatments involves hormonal injections to stimulate the ovaries so that they cause the eggs to be released for fertilisation.
These treatments can cause the ovaries to become larger during ovulation , and go back to normal size once the ovulation phase is over. The size of the ovaries increases during pregnancy as they produce the hormones oestrogen and progesterone that aid in pregnancy. However, it is prudent to have your doctor check and ensure that you do not have cysts or fibroids.
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