Automated Detection Method for Clustered Microcalcification in Mammogram Image Based on Statistical Textural Features

Abstract

Abstract-Breast cancer is the most frightening cancer for women in the world. The current problem that closely related with this issue is how to deal with small calcification part inside the breast called micro calcification (MC). As a preventive way, a breast screening examination called mammogram is provided. Mammogram image with a considerable amount of MC has been a problem for the doctor and radiologist when they should determine correctly the region of interest, in this study is clustered MC. Therefore, we propose to develop an automated method to detect clustered MC utilizing two main methods, multi-branch standard deviation analysis for clustered MC detection and surrounding region dependence method for individual MC detection. Our proposed method was resulting in 70.8% of classification rate, then for the sensitivity and specificity obtained 79% and 87%, respectively. The gained results are adequately promising to be more developed in some areas.

A New CAD System for Breast Microcalcifications Diagnosis

“…In fact, using a neural network provided a recognition rate of 70.8% with textural primitives in [2] and 84% with morphologic features in [63].…”

“…Then, a white top-hat (ToHb) and a black top-hat (ToHn) transforms are separately applied to L(x, y). These transformations are given by (1) and (2)…”

Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine

“…www.ijarai.thesai.org We also had conducted the similar work. However, previous work's result on classification utilizing neural network was deficient [5]. This system needs a new method to achieve better performance of classification, as well as sensitivity and specificity results.…”