Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering

TL;DR

This paper presents a novel computer-aided detection algorithm for microcalcifications in mammograms, combining wavelet-based image enhancement with fuzzy shell clustering to improve early breast cancer diagnosis.

Contribution

It introduces a new detection method integrating multiresolution wavelet analysis and fuzzy shell clustering, enhancing microcalcification detection accuracy.

Findings

Effective detection of microcalcifications demonstrated

Improved diagnostic support for radiologists

Enhanced image quality through wavelet transform

Abstract

Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.