The amount of tissue available in the breast, commonly characterized by the breast density, is highly correlated with breast cancer. In fact, dense breasts have higher risk of developing breast cancer. On the other hand, breast density influences the mammographic interpretation since it decreases the sensitivity of breast cancer detection. This sensitivity decrease is due to the fact that both ...

Breast cancer as a medical condition and mammograms as images exhibit many dimensions of variability across the population. Similarly, the way diagnostic systems are used and maintained by clinicians varies between imaging centres and breast screening programmes, and so does the appearance of the mammograms generated. A distributed database that reflects the spread of pathologies across the pop...

Radiologists can use the differences between the left and right breasts, or asymmetry, in mammograms to help detect certain malignant breast cancers. An image similarity method is introduced to make use of this knowledge base to recognize breast cancer. Image similarity is determined using a contextual and then a spatial comparison. The mammograms are filtered to find the most contextually sign...

Computer aided diagnostic systems can assist radiologist in detecting breast cancer at an early stage with improved mammogram interpretation efficiency. In this paper, six fractal based features obtained from the fractal dimension computed using differential box counting method, are used for distinguishing between normal mammograms from the cancerous ones. The new fractal feature f6 derived fro...

Mammograms are one of the most widely used techniques for preliminary screening of breast cancers. There is great demand for early detection and diagnosis of breast cancer using mammograms. Texture based feature extraction techniques are widely used for mammographic image analysis. In specific, wavelets are a popular choice for texture analysis of these images. Though discrete wavelets have bee...

Breast cancer is a most common disease diagnosed in women. The Microcalcification Clusters (MCs) in the mammograms are one of the important early sign. The accurate detection of microcalcifications is a key problem in Computer Aided Detection (CAD). In this paper, we have proposed a novel association rule mining approach for classification of microcalcifications. Initially, the shape features a...

Our aim is to establish accurate correspondences between temporal mammograms in order to improve the detection of tumours. We propose a method for detecting abnormalities after aligning a pair of mammograms using thin-plate spline interpolation based on corresponding points on the breast boundary. We also suggest a method for the automatic detection of landmarks within the breast tissue. We sho...

We present first results from a new automated algorithm for the volumetric measurement of the composition of breast tissue from digital mammograms. The new algorithm overcomes issues in previous implementations through better segmentation and use of additional information We measure the success of the new algorithm using an overall quality metric based upon the results from a large multi-site, ...