The aim of this research is the development of a reliable tool to detect early signs of breast cancer in mammographic images. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death of female worldwide. Mammogram is one of the most excellent technologies currently being used for diagnosing breast cancer. In this paper, the Enhanced Artificial Bee Colony Optim...

ss as: Z.A rnal of and host s.2013.0 Abstract In this paper, we propose a novel algorithm to detect the suspicious regions on digital mammograms that based on the Fisher information measure. The proposed algorithm is tested different types and categories of mammograms (fatty, fatty-glandular and dense glandular) within mini-MIAS database (Mammogram Image Analysis Society database (UK)). The pro...

This paper presents an evaluation and comparison of the performance of three different feature extraction methods for classification of normal and abnormal patterns in mammogram. Three different feature extraction methods used here are intensity histogram, GLCM (Grey Level Co-occurrence Matrix) and intensity based features. A supervised classifier system based on neural network is used. The per...

Mammography has been one of the most reliable methods for early detection of breast cancer. There are different lesions which are breast cancer characteristic such as microcalcifications, masses, architectural distortions and bilateral asymmetry. One of the major challenges of analysing digital mammogram is how to extract efficient features from it for accurate cancer classification. In this pa...

To enable tissue function-based tumor diagnosis over the large number of existing digital mammography systems worldwide, we propose a cost-effective and robust approach to incorporate tomographic optical tissue characterization with separately acquired digital mammograms. Using a flexible contour-based registration algorithm, we were able to incorporate an independently measured two-dimensional...

Little is known about the breast cancer risk factors or mammogram characteristics among Native-American women. Southwestern Native-American women have a low risk of breast cancer and a high risk of diabetes. Our purpose was to determine the prevalence of known clinical risk factors for breast cancer and their association with mammogram density in a sample of Southwestern Native-American women u...

In recent years, the stage determining and classifying the mammogram as Benign or Malignant is somewhat complicated process in the medical research. In the earlier papers many classification techniques, CAD designs and feature extraction methods are used constantly for mammogram classification, and has its own advantages and limitations. To overcome the limitations, in this paper a novel approa...

This paper developed a CAD (Computer Aided Diagnosis) system based on neural network and a proposed feature selection method. The proposed feature selection method is Maximum Difference Feature Selection (MDFS). Digital mammography is reliable method for early detection of breast cancer. The most important step in breast cancer diagnosis is feature selection. Computer automated feature selectio...

In this paper, we propose a method to classify two breast lesions – mass and calcification, despite their benignancy or malignancy. Applied to a reference database that provides images with the ground truth set, the evaluation of the method is easy and trustful. This database, from the IRMA project, was developed from the union from of: The Digital Database for Screening Mammography (DDSM), The...

Automatic digital mammograms reading become highly enviable, as the number of mammograms to be examined by physician increases enormously. It is premised that the computer aided diagnosis system is mandatory to assist physicians/radiologists to achieve high efficiency and productivity. To handle uncertainties of medical images, fuzzy soft set theory has been merely scrutinized, even though the ...