Ovarian Follicle Detection for Polycystic Ovary Syndrome using Fuzzy C-Means Clustering
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - July Issue 2013 by IJCTT Journal|
|Volume-4 Issue-7 |
|Year of Publication : 2013|
|Authors :Ashika Raj|
Ashika Raj"Ovarian Follicle Detection for Polycystic Ovary Syndrome using Fuzzy C-Means Clustering"International Journal of Computer Trends and Technology (IJCTT),V4(7):2146-2149 July Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - In this paper, follicles are detected in the ultrasonic images of ovary. PCOS is an endocrine disorder affecting women of reproductive age. This syndrome is mainly seen in women whose age is in between 25 and 35. We are proposing methods for identifying whether a person is suffering from Polycystic Ovary Syndrome (PCOS) or not. Ultrasound imaging of the follicles gives important information about the size, number and mode of arrangement of follicles, position and response to hormonal stimulation. A thresholding function is applied for denoising the image in the wavelet domain. Before the segmentation process the ultrasonic image is preprocessed using contrast enhancement technique. Morphological approach is used for implementing contrast enhancement. This is performed inorder to improve the clarity and quality of the image. Fuzzy c-means clustering algorithm is applied to the resultant image. Finally the cysts are detected with the help of clusters. Cysts are follicles which have abnormal size. Based on the detection of follicles, the suspected patient can be treated as normal or polycystic.
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Keywords : — Polycystic Ovary Syndrome,Denoising, Soft thresholding, Contrast Enhancement, Morphological Operations, Tophat filtering, Segmentation, Fuzzy C-Means Clustering.