FAST DETECTION OF MASSES IN MAMMOGRAMS WITH DIFFICULT CASE EXCLUSION

Gabor Takacs,Bela Pataki

doi:10.47839/ijc.4.3.365

Abstract

Breast cancer is one of the most common forms of cancer among women. Currently mammography is the most efficient method for early detection. A simple and fast mammographic mass detection system and two different methods for difficult case exclusion are presented in this paper. The mass detection system uses a modified version of a known algorithm for small masses and a new algorithm for large masses. The first difficult case filtering method is based on tissue density estimation, the second one on mass candidate count. The system was tested with 600 mammographic cases, each containing 4 images. Case-level performance was measured for malignant mass detection first without and then with difficult case exclusion.

Highlights

Breast cancer is one of the most frequent cancerous diseases among women
An output pixel position was counted as a false mark (FM) if it was not inside the radiologist-drawn boundary of any mass
A mammographic mass detection system and two algorithms for difficult case exclusion were presented in this paper

Summary

INTRODUCTION

Breast cancer is one of the most frequent cancerous diseases among women. Every 12th woman suffers from this disease at least once in her lifetime [1]. Mammography (X-ray examination of the breast) is the most efficient method for early detection. The most important mammographic symptoms of breast cancer can be divided into two main classes:. This AFUM algorithm variant slightly differs from the original one, because in the original algorithm the minimal intensity at distance less than or equal r1 from (x, y) is compared to intensity values at distance r2 from (x, y). Two size classes were defined and different algorithms are used for small and large mass detection. A tissue density estimation based and a mass candidate count based method were developed and tested in the difficult case filtering experiments

DETECTION OF SMALL MASSES

DETECTION OF LARGE MASSES

DIFFICULT CASE EXCLUSION BASED ON TISSUE DENSITY ESTIMATION

DIFFICULT CASE EXCLUSION BASED ON MASS CANDIDATE COUNT

RESULTS

CONCLUSION