Automatic Application Level Set Approach in Detection Calcifications in Mammographic Image

TL;DR

This paper presents an automatic CADe system for mammogram analysis using level set segmentation, texture features, and machine learning classifiers to assist radiologists in detecting breast calcifications.

Contribution

It introduces a novel level set-based segmentation method combined with texture analysis and classification for improved calcification detection in mammograms.

Findings

Detection accuracy between 60% and 70%

Effective segmentation with level set approach

Potential to aid radiologists in diagnosis

Abstract

Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours (KNN) are adopted to distinguish different ACR (American College of Radiology) classification. Moreover, we use the Digital Database for Screening Mammography (DDSM) for experiments and these results in term of accuracy varied between 60 % and 70% are acceptable and must be ameliorated to aid radiologist.