Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images

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1 Asian Journal of Computer Science and Technology ISSN: Vol.7 No.2, 2018, pp The Research Publication, Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images A. Saravanan 1 and S. Sathiamoorthy 2 1&2 Assistant Professor, Division of Computer and Information Science Annamalai University, Annamalai Nagar, Tamil Nadu, India ks_sathia@yahoo.com 2 Corresponding Author Abstract -PCOS is an endocrine disorder attacking women of reproductive age. This symptom has mainly seen in women whose age is in between 25 and 35. Image description based on superpixels has become essential for increasing performance in Computer Vision systems. Depth estimation, segmentation, body model estimation and Object recognition are some critical problems where superpixels can implement. However, superpixels can determine the effectiveness of the system positively or negatively, depending on how high they recognize the object boundaries in the image. Without identifying the right region of the follicle, the risk severity of the patient cannot reveal. In this paper, a new Image denoising methodology for the detection of the follicle in the PCOS has proposed by combing the Simple Linear Iterative Clustering and Fuzzy C Means clustering. Keywords: Polycystic Ovarian Syndrome, Noise Removal, Superpixel clustering method, Gradient-based approach, Fuzzy C Means clustering 1. INTRODUCTION One of the main focuses of interest in image processing, medical imaging. With the advancement of technology, along with increasing the size and resolution of the images, their size has also increased. In such a way that today three dimensional or four-dimensional images have used. There are sections on medical images, which is very important in medical diagnosis. Visual information transmitted in the form of digital images is becoming an essential method of communication in the modern age, but the image obtained after transmiss ion has often corrupted with noise. The received image needs processing before it can used in applications. Image denoising involves the manipulation of the image data to produce a visually high-quality image. II. POLYCYS TIC OVARIAN SYNDROME Follicles [1] are fluid-filled sacs seen inside the ovary. The ultrasonographic morphology of a polycystic ovary (PCO) has characterized by the presence of 12 or more ovarian follicles which are 2-9 mm in size. These follicles have termed as cysts. They are arranged peripherally inside the ovary of a PCOS patient. The symptoms of PCOS are menstrual irregularity, obesity, hyperandrogenism, diabetes, acne, increased risk of cardiovascular disease, male-pattern facial and bodily hair growth and balding, excess ive production of male hormones, infertility, etc. Diagnostic ultrasound uses frequency between 2 and 15 MHz. Ultrasonic waves are produced from the transducer and penetrate into the body tissues, and when the wave reaches an object or a surface with the different texture or acoustic nature, some fraction of the energy has reflected. The echoes so produced are received by the apparatus and changed into electric current. These signals are then amplified and processed to get displayed on CRT (Cathode Ray Tube) monitor. The image so obtained is called an ultrasound scan, and the process is known as ultrasonogram. This image has given as the input. Ultrasound imaging technique [2] is inexpensive and is very active for cyst recognition. The overall quality of the ultrasound image is the end product of a combination of many factors originating from the imaging system and the performance of the operator. An ultrasonic image may contain noises due to loss of proper contact or air gap between transducer and body part. Noises can also be formed during signal processing or beamforming process. The noises may cause the image blurred and thereby lead to poor segmentation. III. SUPERPIXELS CLUSTERING METHOD Superpixel has generally defined as a small group of pixels with similar color. It has been extensively used in various scenarios of computer vision, such as image segmentation and object recognition. Compared to the traditional pixel representation in image, the superpixel representation greatly reduces the number of image primitives and thus improves the representative efficiency [3]. Moreover, it is convenient and effective to compute the region-based visual features with the superpixels, which will simplify the succedent vision tasks like object recognition. Furthermore, the regions extracted by the superpixel over-segmentation usually form a more compact representation of an image than the original pixel grid [4]. In the field of computer vision and image processing, the preprocessing stage is an important stage. And superpixels generation has attracted substantial attention during the last period. The superpixel concept was originally presented by Ren and Malik [3] as the perceptually uniform regions using 118

2 Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images the normalized cuts (NCuts) algorithm. Superpixels are clusters of pixels which share similar features, thus they can be used as mid-level units to decrease the computational cost in many vision problems, such as image/video segmentation [4][5][6][7][8] saliency, tracking, classification [9], object detection [10], motion estimation [11], reconstruction [12], and other vision applications [13]. 1. Normalized Cut 2. Simple Linear Integration Clustering (SLIC) 3. Linear Spectral Clustering (LSC) 4. Entropy Rate (ERS) Each algorithm has its own advantage and disadvantage for superpixel segmentation, however, it is still very challenging to develop a high quality and real-time superpixel algorithm that exhibits the properties including good boundary adherence, compact constraints, regular shapes and low computational complexity. Superpixels are used to replace pixels for a more compact visual representation together with fast computation. As an important preprocessing step of a large number of image processing applications, its computational cost is the most concerned issue. Among these superpixel algorithms, the SLIC algorithm becomes popular, since it can produce superpixels quickly without sacrificing much of the segmentation accuracy. But there is still much room for the improvement of superpixel in computational cost and adherence to boundaries. A. Simple Linear Iterative Clustering (SLIC) Method Simple linear iterative clustering (SLIC) [3] adopts a K- means clustering approach to generate superpixels efficiently. SLIC superpixels correspond to clusters in the labxy feature space. It has two parameters, the desired number of approximately equally sized superpixels k, and a parameter m to offer control over their compactness. Its complexity is linear in the number of pixels N and independent of the number of superpixels k. The following steps involved in the SLIC algorithm as: Step 1: Firstly, the input image has converted to the CIELAB color space. Step 2: Then, a total of k initial cluster centers, - are sampled on a regular grid spaced pixels apart. Step 3: Optionally, the centers may be moved to the lowest gradient position in a 3 X 3 neighborhood, to avoid initialization in a noisy pixel. Step 4: Next, in the assignment step, each pixel is associated with the nearest cluster center according to a distance measure D, but considering only the centers whose search region of 2S 2S pixels overlaps its location. Step 5: After that, an update step adjusts the cluster centers to be the mean, - vector of all the pixels belonging to the cluster. Step 6: The assignment and update steps have then repeated for a total of 10 iterations. Step 7: At the end, some disjoint pixels that do not belong to the same connected component as their cluster center may remain. Therefore, a post-processing step to enforce connectivity is applied, by assigning a distinct label to each connected component. The distance measure D is given by: ( ) where m gives the relative importance among the color distance (d c ) and spatial distance (d s ). When m is large, the resulting superpixels are more compact, whereas, when m is small, we have better adhesion to the image boundaries, but with less regular size and shape. IV. PROPOSED IMAGE DENOIS ING METHODOLOGY FOR DETECTION OF FOLLICLE In the proposed pre-processing clustering algorithm, the following steps are involved for the image denoising of the follicle in women with PCOS. Step 1: Input image Y with white Gaussian noise. Step 2: Set parameters: noise variance δ, superpixles number Ns, cluster number K. Step 3: This procedure contains the initial superpixel by SLIC algorithm. Input: Image I, superpixel p, threshold and the candidate set C set ;, labeled set L set Output: Initial superpixel label L (p); Step 3.1: Set initial pixel label is 0 for each image in I; Step 3.2: while each pixel have a new label do Step 3.3: find a seed k; Step 3.4: set k L set Step 3.5: while L set is empty or the number of pixels in p is larger than the threshold S/N do Step 3.6: for each pixel j in L set do Step 3.7: for each pixel i around pixel j do Step 3.8: compute and clustering distance ( ) and with seed k and j Step 3.9: If ( ) then Step 3.10: set i Step 3.11: end if Step 3.12: end for Step 3.13: end for Step 3.14: set L set = C set Step 3.15: end while Step 3.16: end while Step 4: Utilize Fuzzy C-Means clustering method to group superpixel into K cluster to form sub-datasets {M k } *

3 A. Saravanan and S. Sathiamoorthy Step 5: This procedure contains the Refinement method for superpixels. It fuses the superpixels. Input: Initial superpixel label L (p); Output: Refined superpixel label ( ) Step 5.1: set distance d(p) = for each superpixel p; Step 5.2: if the number of pixels in p is less than S/N then Step 5.3: for each superpixel l around p do Step 5.4: compute the fusing distance ( ) between p and l; Step 5.5: if ( ) ( ) then Step 5.6: set ( ) ( ); Step 5.7: set label j = l; Step 5.8: endif Step 5.9: end for Step 6: Repeat till the entire superpixels end. Step 7: Reconstruct the image and output the denoised image Y. V. RESULT AND DISCUSSION For evaluating the proposed algorithm, the performance of the Image Denoising Methodology has compared with the following filter methods like Median filter and other denoising methods like K-means Singular Vector Decomposition (K-SVD). The parameters set for the proposed Image Denoising methodology has followed as the superpixels number Ns has set to 500, the cluster number K has set to 60, and the noise variance in the range of [5, 15, 25, 40, 60]. The iteration number is set based on the noise level, and it requires more iteration for a higher noise level. The performance metrics like PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity), and FOM (Figure of Merit) are used to validate the proposed Image Denoising methodology. The above tables 1 - table 5 represent the values of PNSR, SSIM, and FOM with the different noise levels for the given methods by using three PCOS images. TABLE II PERFORMANCE ANALYSIS OF THE MEDIAN FILTER, K-SVD AND PROPOSED METHODOLOGY OF INPUT IMAGES AT THE NOISE VARIANCE RANGE OF 5% Input Image of noise variance = 5% PSNR SSIM FOM PSNR SSIM FOM PSNR SSIM FOM T ABLE II PERFORMANCE ANALYSIS OF THE MEDIAN FILTER, K-SVD AND PROPOSED METHODOLOGY OF INPUT IMAGES AT THE NOISE VARIANCE RANGE OF 15% Input Image of noise variance = 15% PSNR SSIM FOM PSNR SSIM FOM PSNR SSIM FOM

4 Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images T ABLE III PERFORMANCE ANALYSIS OF THE MEDIAN FILTER, K-SVD AND PROPOSED METHODOLOGY OF INPUT IMAGES AT THE NOISE VARIANCE RANGE OF 25% Input Image of noise variance = 25% PSNR SSIM FOM PSNR SSIM FOM PSNR SSIM FOM T ABLE V PERFORMANCE ANALYSIS OF THE MEDIAN FILTER, K-SVD AND PROPOSED METHODOLOGY OF INPUT IMAGES AT THE NOISE VARIANCE RANGE OF 40% Input Image of noise variance = 40% PSNR SSIM FOM PSNR SSIM FOM PSNR SSIM FOM T ABLE V PERFORMANCE ANALYSIS OF THE MEDIAN FILTER, K-SVD AND PROPOSED METHODOLOGY OF INPUT IMAGES AT THE NOISE VARIANCE RANGE OF 60% Input Image of noise variance = 60% PSNR SSIM FOM PSNR SSIM FOM PSNR SSIM FOM VI. CONCLUS ION The varying noise levels are utilized to certify the quality of the proposed denoising method which has perceived as Image Denoising Methodology for the detection of the follicle. This proposed method is applied to exclude the noise in the presented input images without changing the efficiency of the segmentation. This proposed technique is also worked well also at the various noise level. From the resultant tables and figures, it has concluded that the proposed method provides the maximum value of PNSR, SSIM, and FOM for the given image even at the noise level 121

5 A. Saravanan and S. Sathiamoorthy of 5%, 15%, 25%, 40% and 60%. This noise removal makes the decision making easier. REFERENCES [1] 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 (RTIPPR 10), pp , [2] M. J. Lawrence, R.A. Pierson, M.G. Eramian and E. Neufeld, Computer assisted detection of polycystic ovary morphology in ultrasound images, In Proc. IEEE Fourth Canadian conference on computer and robot vision (CRV 07), pp , [3] X. Ren and J. Malik, Learning a classification model for segmentation, Proceedings of the IEEE International Conference on Computer Vision, IEEE Computer Society, October 13-16, pp.10-17, [4] Angala parameswari Rajasekaran and P. Senthilkumar., Image Denoising Using Median Filter with Edge Detection Using Canny Operator, International Journal of Science and Research (IJSR), Vol. 3, No. 2, pp.30-34, February [5] Hiroyuki Takeda, Sina Farsiu and Peyman Milanfar, Kernel Regression for Image Processing and Reconstruction, Ieee Transactions On Image Processing, Vol. 16, No. 2, pp ,2007. [6] Connelly Barnes, et al. "The generalized patchmatch correspondence algorithm." European Conference on Computer Vision. Springer, Berlin, Heidelberg, [7] Raman Maini and Himanshu Aggarwal. "Study and comparison of various image edge detection techniques." International journal of image processing (IJIP), Vol. 3, No. 1, pp. 1-11, [8] Sébastien Drouyer, et al. "Sparse Stereo Disparity Map Densification using Hierarchical Image Segmentation." International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing. Springer, Cham, [9] Sivan Harary, et al. "Image segmentation." U.S. Patent No. 9, 300, Mar [10] Joseph JO Ruanaidh,. "System for preparing an image for segmentation." U.S. Patent No. 9,275, Mar [11] S. Mahaboob Basha and M. Kannan, "Design and implementation of low-power motion estimation based on modified full-search block motion estimation." Journal of Computational Science, [12] Kan, Andrey. "Machine learning applications in cell image analysis." Immunology and Cell Biology,