Follicle Segmentation using K-means Clustering from Ultrasound Image of Ovary

EasyChair Preprint 2543

Abstract

Detection of number, shape and size of follicles in the ovary can play an important role in diagnosis and monitoring of different diseases like infertility, PCOS (Polycystic Ovarian Syndrome), ovarian cancer etc. Now-a-days the identification of these characteristics of follicles is done manually by radiologists and doctors from the Ultrasound Images of ovaries. Sometimes manual analysis can be a tedious and error prone job. In this paper a method is proposed for automatic segmentation of follicles from Ultrasound Images using the K-means clustering technique.

Keyphrases: K-means clustering, Ovary, Segmentation, Ultrasound images, follicle detection

@booklet{EasyChair:2543,
  author    = {Ardhendu Mandal and Debasmita Saha and Manas Sarkar},
  title     = {Follicle Segmentation using K-means Clustering from Ultrasound Image of Ovary},
  howpublished = {EasyChair Preprint 2543},
  year      = {EasyChair, 2020}}