Neural network based microcalci ication etection in mammographic CAD system

Abstract

IEEE InDmational Wmkshp on lnteiligrm O m Acqluritian and Adwinced Computing Systcm: Tschnoiogy and Applications 8.10 scptunber m3, Lvi", ULraine Abstract: This document presents the computer aided diagnosis system being developed to help experts in screening mammography. It is a very important project because about 8 % of women develop breast cancer in her lifetime therefore global screening is necessary. It means that reliable diagnosis of huge number of images musi be solved. The basic architecture of the system and the information processing needed is presented. One of the most important tasks in mammographic diagnosis systems is microcalc@cation detection. It is solved by a hierarchical neural architecture. The original suggestion of that sirncture was improved by two ways. The image features .used as inputs io the neural neiworks were analyzed and the feature set was extended. The neural architecture was embedded in a neural ensemble context for improving ihe quality of the solution further. Results ofthe tests of that detection procedure show that the false detection ratios are acceptable.

Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images

“…They can successfully detect the MCs in the USF database with 93.75% TP and 6.255% FP. Lasztovicza et al [18] introduced a hierarchal neural network to detect the MCs in the mammogram image. In their method, the input image was sampled and the image pyramid is built using multi-scale method.…”