In this study we present a pre-CAD system that aims to help the radiologists in the analysis of the high number of mammograms that they have to evaluate each day, helping to prevent the increased number of misclassification that could happen, due to the repetitive task to which they are submitted. The method consists in extracting features from mammograms, previously classified by experts accor...

Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The pro...

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...

Digital mammogram has become the reliable and most effective screening method for the early detection of breast cancer. A novel Fractal Hurst-based Gamma Transformation (FHGT) is presented in this paper for the segmentation of masses from mammograms. This method is a composition of two mechanisms namely detection of masses from digital mammograms and the segmentation of those detected masses. T...

Mammography is the most effective procedure for the early detection of breast cancer. The segmentation of mammograms plays a major role in isolating areas which can be subject to tumors. The identification of these zones is generally done in three stages: pectoral muscle segmentation, hard density zone detection and texture analysis of regions of interest. In this paper, a novel algorithm for d...

As an ongoing effort to develop a computer aided system for the detection of masses on mammograms, we propose on this work a new model-based segmentation algorithm. The algorithm is based on a template matching scheme by using the mutual information approach in the similarity metric. Thus, the system will be able to determine if it exists a true mass on the studying image. The proposal was deve...

The ability to detect small tumors is impaired in dense mammograms. It has been suggested that the sensitivity of mammograms could be lower in mammograms obtained during the luteal phase of the menstrual cycle. We examined the change in mammographic density from the follicular to the luteal phase of the menstrual cycle in 11 women. Although the average increase in densities was quite small (1.2...

BACKGROUND Black and Hispanic women are diagnosed at a later stage of breast cancer than white women. Differential access to specialists, diffusion of technology, and affiliation with an academic medical center may be related to this stage disparity. METHODS We analyzed data from a mammography facility survey for the metropolitan region of Chicago, Illinois, to assess in part whether quality ...

OBJECTIVE Matrix gla protein is a vitamin K-dependent inhibitor of medial arterial calcification whose synthesis and activity are blocked by warfarin. Warfarin induces arterial calcification in experimental models, but whether this occurs in humans is unclear. This was addressed by examining breast arterial calcification, which is exclusively medial and easily identified on mammograms. APPROA...

Suspicious region segmentation is one of the most important parts CAD systems that are used for breast cancer detection in mammograms. In a system, there can be so many suspicious regions determined mammogram because complex structure breast. This study proposes hybrid thresholding method to use efficient mammograms and reducing number regions. The proposed provides fully-automatic based on det...