Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome ImagesAuthor : A. Saravanan and S. Sathiamoorthy
Volume 7 No.2 July-September 2018 pp 118-122
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.
Polycystic Ovarian Syndrome, Noise Removal, Superpixel clustering method, Gradient-based approach, Fuzzy C Means clustering
 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. 120-125, 2010.
 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. 105-112, 2007.
 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, 2003.
 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 2014.
 Hiroyuki Takeda, Sina Farsiu and Peyman Milanfar, “Kernel Regression for Image Processing and Reconstruction”, Ieee Transactions On Image Processing, Vol. 16, No. 2, pp.349-366,2007.
 Connelly Barnes, et al. “The generalized patchmatch correspondence algorithm.” European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2010.
 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, 2009.
 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, 2017.
 Sivan Harary, et al. “Image segmentation.” U.S. Patent No. 9, 300, 828. 29 Mar. 2016.
 Joseph JO Ruanaidh,. “System for preparing an image for segmentation.” U.S. Patent No. 9,275,465. 1 Mar. 2016.
 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, 2016.
 Kan, Andrey. “Machine learning applications in cell image analysis.” Immunology and Cell Biology, 2017.