Follicle Detection in Digital Ultrasound Images using Bidimensional Empirical Mode Decomposition and Fuzzy C-means Clustering Algorithm

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1 Follicle Detection in Digital Ultrasound Iages using Bidiensional Epirical Mode Decoposition and Fuzzy C-eans Clustering Algorith M.Jayanthi Dr.R.Kiran Kuar Research Scholar, Departent of CS, Krishna University, Machilipatna, India. # Departent of Coputer Science, Krishna University, Machilipatna, India. Abstract: Ultrasound Iaging is one of the technique used to study inside huan body with iages generated using high frequency sounds waves. The applications of ultrasound iages include exaination of huan body parts such as Kidney, Liver, Heart and Ovaries. This paper ainly concentrates on ultrasound iages of ovaries. The detection of follicles in ultrasound iages of ovaries is concerned with the follicle onitoring during the diagnostic process of infertility treatent of patients.monitoring of follicle is iportant in huan reproduction. This paper presents a ethod for follicle detection in ultrasound iages using Bi-diensional Epirical Mode Decoposition and Fuzzy C-eans clustering algorith. Bi-diensional Epirical Mode decoposition is used for contrast enhanceent of ovary iage and Fuzzy c-eans algorith is used to segent the ovary iage. After segentation, using the region properties of the iage, the follicles in the ovary iage are identified. The proposed algorith is tested on saple ultrasound iages of ovaries for identification of follicles and with the region properties, the ovaries are classified into three categories, noral ovary, cystic ovary and polycystic ovary with its properties. The experient results are copared qualitatively with inferences drawn by edical expert anually and this data can be used to classify the ovary iages. Keywords: Ovarian Classification, Iage Processing, Histogra Equalization, Bi-diensional Epirical Mode Decoposition. 1. INTRODUCTION PCOS (Polycystic Ovarian Syndroe) is one of the coon disorder seen in feales of reproductive age. The disorder is characterized by the foration of any follicular cysts in the ovary. The ain cause of this disorder in feales is due to enstrual probles, hirsutis, endocrine abnoralities, acne, obesity etc [1]. The detection of ovarian follicle is done using ultrasound iages of ovaries. Object recognition in an ultrasound iage is a challenging task which includes the detection of follicles in ovary, growth of the foetus, onitoring of proper developent of the foetus and presence of tuor [2]. Now a days the diagnosis perfored by doctors is to anually count the nuber of follicular cysts in the ovary, which is used to judge whether PCOS exists or not. This anual counting ay lead to probles of variability, reproducibility and low efficiency. Autoating this echanis will resolve these probles. In literature, less work done in autoating PSOC diagnosis. In case of noral ovary, under influence of right levels of horones FSH and LH (i.e. Follicle Stiulating Horone and Luteinizing Horone), only one follicle grows in size to about 20 in diaeter, atures and becoes ready for ovulation. In PCOS affected ovary, due to reduced levels of FSH and LH and high levels of prolactine, follicles fail to grow and attain aturity. Thus, in ultrasound iage of PCOS affected ovary, large nuber of sall follicles (typically 12 or ore and about 2-9 in diaeter) can be seen distributed along the periphery of the ovary, classically described as 'necklace foration [3]. Moreover, the ovarian volue in such patients is typically increased over 10 c 3. Figure 1 shows ultrasound iage of noral ovary showing only one follicle ready for ovulation. Whereas, figure 2 shows nuerous sall follicles present along the periphery of the ovary [4]. In this paper, an algorith for identification of follicles in ovarian iages is presented using Bi-diensional Epirical Mode Decoposition and Fuzzy c-eans clustering algorith. The experient results are copared with inferences drawn by edical expert anually and identifies the ovary iages ore accurately than anual segentation. 2905

2 The paper is organized as follows: Section II presents contrast Enhanceent using Bi-diensional Epirical Mode Decoposition (BEMD), Section III presents Fuzzy C-eans clustering algorith (FCM), Section IV presents Identification of follicles in Ovary Ultrasound Iages using BEMD and FCM, Section V presents Experiental results, and finally Section VI reports conclusion. Figure 1: Noral Ovary Figure 2: PCOS affected Ovary 2. CONTRAST ENHANCEMENT USING BI- DIMENSIONAL EMPIRICAL MODE DECOMPOSITION The EMD proposed by Dr. Norden Huang [5], was a technique for analyzing nonlinear and non-stationary signals. It serves as an alternative to ethods such as wavelet analysis and short-tie Fourier transfor. It decoposes any coplicated signal into a finite and often sall nuber of Bi-diensional Intrinsic Mode Functions (IMF). The IMF is syetric with respect to local zero ean and satisfies the following two conditions. 1. The nuber of extrea and the nuber of zero crossings ust either be equal or differ by one. 2. At any point, the ean value of the envelope defined by local axia and local inia is zero, indicating the function is locally syetric. The decoposition ethod in EMD is called Shifting Process [6]. The shifting process of the twodiensional signal such as iage can be adapted fro the one-diensional signal as follows. 1. Let I original is defined as an iage to be decoposed. Let j=1 (index nuber of IMF), I= I original (the residue). 2. Identify the local axia and local inia points in I. 3. By using interpolation, create the upper envelope E up of local axia and the lower envelope E lw of local inia. 4. Copute the ean of the upper envelope and lower envelope. E ean = [E up + E lw ]/2 (1) 5. I if = I- E ean. (2) 6. Repeat steps 2-5 until I if can be considered as an IMF. 7. IMF(j)= I if, j=j+1, I = I- I if, (3) 8. Repeat steps 2-7 until all the standard deviation of two consecutive IMFs is less than a predefined threshold or the nuber of extrea in I is less than two. The first few IMFs obtained fro BEMD contain the high frequency coponents which correspond to salient features in original iage and the residue represents low frequency coponent in the iage. The original iage can be recovered by inverse BEMD as follows [7]: I = RES+ IMF ( j) j (4) The BEMD process for generation of IMFs of the ovary ultrasound iage are shown in figure 3. Iage Contrast Enhanceent: If the contrast of the ovary iage is low, the quality of the edges of follicles extracted fro the ovary iage will be poor. The quality of the ovary iage can be iproved by applying this BEMD based contrast enhanceent algorith to the original iage prior to the coputation of identification of follicles. i. First divide the Ovary iage into Intrinsic Mode Functions (IMF) by using Bidiensional Epirical Mode 2906

3 ii. Decoposition. BEMD F if1 if 2 if n ( ) [,,..., ].\ The enhanced iage is obtained by the suation of IMFs, with each IMF ultiplied by weight w i. The suation of the weights should be equal to one. Ultrasound Ovary Iage n EMI wi* if i1 (5) w Where i denotes the weight of ith if and EMI represents the enhanced icroarray iage. Ultrasound Ovary Iage (Contrast Enhanced) i IMF1 IMF2 IMF3 IMF4 Figure 3: IMF of ultrasound iage 3. FUZZY C-MEANS CLUSTERING ALGORITHM The Fuzzy c-eans clustering algorith [8] for segentation of ovary iage is described as follows: 1. Randoly consider K initial clusters {C 1, C 2,,C k } fro the *n iage pixels {I 1, I 2, I 3,,I *n }. 2. The ebership atrix u ij is initialized with value fro 0 to 1 and value of =2. The suation of pixel eberships representing particular cluster should be equal to Assign each pixel to the cluster C j {j=1,2,..k} if it satisfies the following condition u D( I, C ) u D( I, C ), q 1,2,..., K ij i j iq i q j q (6) Where D(.,.) is the Euclidean distance easure between two values. 4. Find new ebership and cluster centroid values as follows 1 uik, for1 i K K 1 D( Ci, Ik) 1 ( ) D( C, I ) j1 j k denotes the k th object in the i th cluster. u ik 2907

4 C ^ i n j1 n j1 u I ij u ij j (7) Where n is the nuber of pixels belonging to cluster C i. 5. Continue 2-3 until each object is assigned to the cluster that has axiu ebership [9]. 4. FOLLICLE IDENTIFICATION IN OVARY ULTRASOUND IMAGE The ain steps of the identification of follicles in ovary iage are sued up as follows: 1. The ultrasound iage is processed by BEMD based contrast enhanceent algorith, then an iage with sharper contrast is obtained. 2. Apply FCM algorith to segent the contrast enhanced ovary iage 3. Convert the segented iage into binary iage and edges are extracted using canny operator. 4. Perfor Matheatical Morphology operations on the resulting iage to reove isolated pixels and to replace thick lines with thin lines. 5. Use ifill function to fill the holes. These holes denote follicles in ovary iage. 6. Using the region properties, we can extract the features of follicles such as ajor axis length, inor axis length, area, centroids etc. With this features, ovaries are classified into noral ovary, cystic ovary and polycystic ovary.. 5. EXPERIMENTAL RESULTS In this section, the proposed ethod is used to detect the follicles in ovary ultrasound iages. Ovary iages are obtained fro the publicly available websites [10]. The iage1 and iage2 is taken fro dataset D1 with size 256*256. The qualitative analysis of the proposed ethod is shown in figure 4 copared with anual segentation. 6. CONCLUSION The inforation about the status of feale reproductive syste is iportant for probles related to fertility and faily planning. The ultrasound iaging is an effective tool in infertility treatent. Monitoring the follicles in ultrasound iages in ters of nuber, size, shape and position is iportant in huan reproductive syste. In this paper, a ethodology of identification of follicles in ultrasound iages is presented. The proposed ethod uses Bi-diensional epirical ode decoposition procedure, to extract first intrinsic ode functions and each IMF is ultiplied by weight w i. The suation of weighted IMFs gives the contrast enhanced iage. After enhanceent, the iage is segented using FCM algorith. By using region properties, the follicle regions in the ultrasound iage are identified. This proposed ethod is copared qualitatively with anual segentation. In future this follicle inforation is used to classify the ovary iages into three classes, noral ovary, cystic ovary and polycystic ovary using the region properties of identified follicles. Ovary Iage 1 Contrast Enhanced using BEMD Segented using FCM Follicle identification 2908

5 Ovary Iage 2 Contrast Enhanced using BEMD Segented using FCM Follicle identification Manual Segentation Ovary Iage 1 Manual Segentation Ovary Iage

6 Figure 4: Follicle detection in ovary ultrasound iage REFERENCES [1] Yinhui Deng, Yuanyuan Wang, Ping Chen, Autoated Detection of Polycystic Ovary Syndroe fro Ultrasound Iages, 30 th Annual International IEEE EMBS Conference, IEEE 2008, PP [2] Kiruthika.V, M.M.Raya, Autoated Segentation of Ovarian Follicle using K- enas Clustering, 2014, Fifth International Conference on Signal and Iage Processing, pp [3] Palak Mehrotra, Chandana Chakraborthy, Biswanath Ghoshdastidar, Autoated Ovarian Follicle recognition for Polycystic ovary syndroe, Proceedings of International conference on Iage Inforation processing, ICIIP [4] Sharvari S.Deshpande, Asita Wakankar, Autoated dectection of Polycystic Ovarian Syndroe using Follicle Recognition, Proceedings of International Conference on Advanced Counication Control and Coputing Technologies, IEEE 2014, pp [5] B.Saichandana, J.Harikiran, Dr.K.Srinivas, Dr.R.KiranKuar, Application of BEMD and Hierarchical Iage Fusion in Hyperspectral Iage Classification, International Journal of Coputer Science and Inforation Security, 2016; 14(5): [6] J.Harikiran, et.al. Multiple Feature Fuzzy C- eans Clustering Algorith for Segentation of Microarray iage, IAES International Journal of Electrical and Coputer Engineering, 2015; 5(5): [7] J.Harikiran, et.al. Fuzzy C-eans with Bidiensional epirical Mode decoposition for segentation of Microarray Iage, International Journal of Coputer Science issues, 2012; 9(5): [8] J.Harikiran, Dr.P.V.Lakshi, Dr.R.Kiran Kuar, Multiple Feature Fuzzy C-eans Clustering Algorith for Segentation of Microarray iage, IAES International Journal of Electrical and Coputer Engineering, Vol. 5, No. 5, pp , October [9] B.Saichandana, Dr.K.Srinivas, Dr.R.Kiran Kuar, Clustering Algorith cobined with Hill clibing for Classification of Reote Sensing Iage, International Journal of Electrical and Coputer Engineering,vol 4, No 6, pp , Deceber [10] P.S.Hireath and Jyothi R.Tegnoor, Follicle Detection and Ovarian Classification in Digital Ultrasound Iages of Ovaries, INTECH [11] Sandy RIHINA et.al. Autoated Algorith for Ovarian Cysts Detection in Ultrasonogra, Proceedings of International Conference on Advances in Bioedical Engineering IEEE [12] Bozidar Potocnik ae.al. Autoated Coputer Assisted Detection of Follicles in Ultrasound Iages of Ovary, IEEE,