What is it about?

In this paper we report on a novel method for automatic recognition of follicles and cysts whose classification rule is based on the intensity values. The method uses discrete wavelet transform for despeckling, clustering for classification, edge based segmentation and image fusion.

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Why is it important?

This kind of study is important because assistance of a computer-based processing algorithm may play an important role on the way to a successful recognition of crucial information contained in the ultrasound recordings of the follicle/cyst. It supports the physician in decision making towards diagnosis and treatment.

Perspectives

This article should be of interest to researchers who work on ovulatory disorders,problems related to infertility and for people working in the medical image processing.

Dr. Kiruthika V
Hindustan Group of Institutions

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This page is a summary of: Automatic Segmentation of Ovarian Follicle Using K-Means Clustering, January 2014, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icsip.2014.27.
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