Breast parenchymal density has been found to be a strong indicator for breast cancer risk [1], however, to date, measures of breast density are qualitative and require the judgement of the radiologist. Objective, quantitative measures of breast density are crucial tools for assessing the association between the risk of breast cancer and mammographic density as well as for quantification of dens...

Computer aided diagnosis (CAD) systems can be used as a second opinion to the radiologists for diagnosis of breast cancer from mammogram images. In this paper, we have proposed preprocessing method to remove noise from mammogram images. Then, enhancement has been performed. After that, background has been removed. Finally, pectoral muscle separation has been performed. It has been noted that re...

A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. Our collaboration, among italian physicists and radiologists, has built a large distributed database of digitized mammographic images and has developed a Computer Aided Detection (CADe) system for the automatic...

The size of a lesion is a feature often used in computer-aided detection systems for classification between benign and malignant lesions. However, size of a lesion presented by its area might not be as reliable as volume of a lesion. Volume is more independent of the view (CC or MLO) since it represents three dimensional information, whereas area refers only to the projection of a lesion on a t...

Introduction The widespread implementation of screening mammography has decreased the mortality of breast cancer by as much as 50% [1]. Despite recent controversies regarding the benefits of screening mammography, and the age at which screening mammography should begin, mammography remains the most widely utilized tool for early detection of breast cancer. However, it is also evident that mammo...

Clusters of Microcalcifications and masses are the signs of breast cancer. They have different shapes and sizes. Globally, clusters and masses have different frequency characteristics. Due to small size of microcalcifications and high intensity of them with compared to their neighbour pixels, microcalcifications correspond to highfrequency components of the image spectrum. On the other hand, ma...

Currently Mammography is a most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast region segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the removal of pectoral muscle are essential pre-processing steps in Computer Aided Diagnosis (CAD...

The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using mass shape pattern. Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass shape pattern was used to categorize the ROI as true...

In this chapter, an overview of recent developments in image analysis and understanding techniques for automated detection of breast cancer from digital mammograms is presented. The various steps in the design of an automated system (i.e. Computer Aided Detection [CADe] and Computer Aided Diagnostics (CADx)] include preparation of image database for classification, image pre-processing, mammogr...