The presence of microcalcification clusters in mammograms contributes evidence for the detection of early stages of cancer. In this paper, a low-cost and high-speed neural network based breast cancer detection algorithm is presented. The microcalcifications are extracted with an adaptive neural network that is trained with cancer/malignant and normal/benign breast mammograms and a best accuracy...

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 must be solved. The basic architecture of the system and the information processing n...

A method is proposed based on the combination of wavelet analysis and principal component analysis (PCA). Microcalcification (MC) candidate regions are initially labeled using area and contrast criteria. Mallat’s redundant dyadic wavelet transform is used to analyze the frequency content of image patterns at horizontal and vertical directions. PCA is used to efficiently encode MC patterns in wa...

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...

Inflammation has a central role in the progression of coronary atherosclerosis. Recent developments in cardiovascular imaging with the advent of hybrid positron emission tomography have provided a window into the molecular pathophysiology underlying coronary plaque inflammation. Using novel radiotracers targeted at specific cellular pathways, the potential exists to observe inflammation, apopto...

Computer Assisted Diagnosis (CAD) is rapidly reaching worldwide acceptance in different fields of medicine. Particularly, CAD has found one of its main applications in breast cancer diagnosis where the detection of microcalcifications in women breasts is typically associated with the presence of cancer. In this paper, a method for automatic breast contour detection is presented as a pre-process...

This research evaluates the denoising abilities of some image-processing filters used in facilitating early detection microcalcifications breast tissues. The mean, median and Gaussian were employed to denoise mammogram images microcalcification phantoms various densities. performances assessed by evaluating mean squared error (MSE), peak signal-to-noise ratio (PSNR), (SNR). All experiments carr...

Introduction Breast cancer is one of the most common types of cancer among women.  Early detection of breast cancer is the key to reducing the associated mortality rate. The presence of microcalcifications clusters (MCCs) is one of the earliest signs of breast cancer. Due to poor imaging contrast of mammograms and noise contamination, radiologists may overlook some diagnostic signs, specially t...