Fuzzy technique for microcalcifications clustering in digital mammograms

Fuzzy technique for microcalcifications clustering in digital mammograms

BACKGROUND Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could t...

Title:fuzzy Technique for Microcalcifications Clustering in Digital Mammograms

System for Automatic Detection of Clustered Microcalcifications in Digital Mammograms

In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle m...

Neural Network Based Technique to Locate and Classify Microcalcifications in Digital Mammograms

This paper proposes a technique that extracts suspicious areas containing microcalcifications in digital mammograms and classifies them into two categories whether they contain benign or malignant clusters. The centroids and radiuses provided by expert radiologist are being used to locate and extract suspicious areas. Neural network’s generalisation abilities are used to classify them into beni...

Wavelet Based Microcalcifications Detection in Digitized Mammograms

Detection of microcalcifications in mammograms has received much attention from researchers and public health practitioners in these last years. The challenge is to quickly and accurately overcome the development of breast cancer which affects more and more women through the world. Microcalcifications appear in a mammogram as fine, granular clusters, which are often difficult to identify in a r...