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Follicle Segmentation from ovarian USG image using Horizontal Window Filtering and Filled Convex Hull Technique

EasyChair Preprint no. 2608

8 pagesDate: February 8, 2020


In this paper a segmentation technique has been developed and discussed to segment different follicles from ultrasound image. The proposed segmentation technique used a 20 pixel long window and standard deviation of the USG image for smoothing and despeckling the image. Further, morphological opening followed by morphological closing operations have been applied to the image for removing the paper and salt noise. Next, segmentation of the follicles is done by finding the active contours and filled convex hull from the intermediate USG image that contains only the follicles those are bright i.e. white in color with a black background. Finally, a comparative study has been presented between the experimental results and inferences made by the experts to validate the results towards determining the degree of accuracy of the proposed technique.

Keyphrases: active contour, convex hull, filled convex hull technique, filtered image, flatten array, follicle, follicle present, follicle segmentation, horizontal window filtering, Image Despeckling, image segmentation, local mean, Medical Imaging, Morphological Opening and Closing, ovarian ultrasound image, ovarian usg image, Ovary, partially filtered usg image, performance evaluation, pixel long window, Salt and Paper Noise, segmentation technique, segmented follicle, shape structuring element, Speckle noise, standard deviation, Ultrasound image, usg image

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ardhendu Mandal and Manas Sarkar and Debasmita Saha},
  title = {Follicle Segmentation from ovarian USG image using Horizontal Window Filtering and Filled Convex Hull Technique},
  howpublished = {EasyChair Preprint no. 2608},

  year = {EasyChair, 2020}}
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