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
Polycystic Ovarian Syndrome is an endocrine ailment affecting women of reproductive age. This syndrome is largely found in women whose age is in between 25 and 35. Without knowing the accurate region of a follicle in ovary, the hazard rigorousness of the patient cannot be exposed. Since, super-pixels can be functional on segmentation and image representation, it has turned out to be essential for refining the competence in computer vision systems. Thus, in this paper, a novel image denoising methodology for the detection of a follicle in the PCOS has been suggested by exploring the super-pixel clustering and Fuzzy C means clustering.
Polycystic ovarian syndrome, Noise removal, Super-pixel clustering method, Gradient-based approach, Fuzzy C means clustering
 P.S.Hiremath and J.R.Tegnoor, “Automated detection of folliclein 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, E. Neufeld, “Computer assisted detection of polycystic ovary morphology inultrasound images,”In Proc. IEEE Fourth Canadian conferenceon computer and robot vision (CRV’07), pp. 105-112, 2007.
 X. Ren, J. Malik, “Learning a classification model for segmentation”, Proceedings of the IEEE International Conference on Computer Vision, IEEE Computer Society, Vol. 13- 16, pp. 10-17, October 2003.
 AngalaparameswariRajasekaran 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, SinaFarsiu andPeymanMilanfar, “Kernel Regression for Image Processing and Reconstruction”, IEEE Transactions on Image Processing, Vol. 16, No. 2, pp.349-366, February 2007.
 Barnes, Connelly, et al.,“The generalized patchmatch correspondence algorithm”,European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2010.
 Maini, Raman, and HimanshuAggarwal, “Study and comparison of various image edge detection techniques”,International Journal of image processing (IJIP) Vol.3, No.1, pp. 1-11, 2009.
 Drouyer, Sébastien, 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.
 Harary, Sivan, et al.,“Image segmentation”, U.S. Patent No. 9,300,828,29 Mar. 2016.
 J. O.Ruanaidh, Joseph, “System for preparing an image for segmentation”, U.S. Patent No. 9,275,465. 1, Mar. 2016.
 Basha, S. Mahaboob, 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.