Saranya, R (2024) Design of automatic follicle detection and ovarian classifcation system for ultrasound ovarian images. Design of automatic follicle detection and ovarian classifcation system for ultrasound ovarian images, 84. pp. 32643-32669.

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Abstract

Polycystic Ovary Syndrome (PCOS) is a common reproductive and metabolic disorder
characterized by an increased number of ovarian follicles. Accurate diagnosis of PCOS
requires detailed ultrasound imaging to assess follicles’ size, number, and position. How�ever, noise often needs to be improved on these images, complicating manual detection
for radiologists and leading to potential misidentifcation. This paper introduces an auto�mated diagnostic system for integration with ultrasound imaging equipment to enhance
follicle identifcation accuracy. The system consists of two main stages: preprocess�ing and follicle segmentation. Preprocessing employs an adaptive Frost flter to reduce
noise, while follicle segmentation utilizes a region-based active contour combined with a
modifed Otsu method. Unlike the conventional Otsu method, where the threshold value
is selected manually, the modifed Otsu method automatically selects initial threshold
values using an iterative approach. After segmentation, features are extracted from the
segmented results. An SVM classifer then categorizes the ovarian image as normal,
cystic, or polycystic. Experimental results demonstrate that the proposed method’s Fol�licle Identifcation Rate is 96.3% and the False Acceptance Rate is 2%, which signif�cantly improves classifcation accuracy, highlighting its potential advantages for clinical
application

Item Type: Article
Uncontrolled Keywords: Follicle · Biomedical instrumentation · Ultrasound image · Active contour · Modifed Otsu · Sensing systems · Accuracy · Detection of PCOS
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Dr. B Sivakumar
Date Deposited: 17 Apr 2026 07:24
Last Modified: 17 Apr 2026 07:24
URI: https://ir.psgcas.ac.in/id/eprint/2802

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