Follicle Segmentation from Ultrasound images of Ovary by using sub-band Entropy-based Wavelet thresholding and object contours

Ultrasonography (USG) images are widely used all over the globe for Gynecological diagnosis of the ovary. USG images are highly contaminated by non-Gaussian multiplicative speckle noises which muffle critical clinical information. Thus despeckling and segmentation of Ovarian USG images has become an inseparable wing of obstetrical diagnosis. The proposed method employs multiresolution discrete wavelet thresholding based on sub-band entropy for eliminating noises. The follicles and other object regions are then beautifully segmented by finding the contour points. Dice Score, Jaccard Similarity Index, Sensitivity, and Specificity measures are used to demonstrate the performance of the proposed method and to illustrate how it outperforms the existing methods available to date.We have achieved state-of-the-art results with Dice Score 95.15%, Sensitivity rate 95.85%, Jaccard Similarity Index 78.9%