Automatic Application Level Set Approach In Detection Calcifications In Mammographic Image

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.

Advances in Cancer Management

“…In this context, we attempt to present an adaptation of Level Set technique for pseudo-detection and investigate two approaches for shape and texture analysis. Therefore, the analysis of texture is used to qualify the density of ROI or to have an idea about the space distribution of micro-calcification (Dheeba & Wiselin, 2010) (Wiesmiller & Chandy, 2010) (Boujelben et al, 2011 (Rangayyan et al, 2006). However, methods defined in the context of angular measures provides so far either of the two categories: Radial Angle (RA) or Turning Angle(TA) (Denise et al, 2008) (Rangayyan et al, 2006).…”

Automatic Diagnosis of Breast Tissue

“…In this context, we attempt to present an adaptation of Level Set technique for pseudo-detection and investigate two approaches for shape and texture analysis. Therefore, the analysis of texture is used to qualify the density of ROI or to have an idea about the space distribution of micro-calcification (Dheeba & Wiselin, 2010) (Wiesmiller & Chandy, 2010) (Boujelben et al, 2011). Generally, texture feature extraction methods can be classified into three major categories; namely statistical, structural and spectral.…”