In this paper, we present a new technique for the detection of spiculated masses in digitized mammograms. The techniques consists of two stages, enhancement of spiculations followed by the detection of the location where they converge. We describe a new algorithm for the enhancement and a new set of linear image filters which we have created for the detection stage. We have tested the algorithm...

We show that dyadic scales may not be sufficient for the detection of masses in mammograms: a lesion may be too blurred on one scale, and then too fragmented at the next. In this paper, we report on the preliminary evidence of our study using a continuous wavelet transform in two dimensions with arbitrary positioning of a wavelet’s center frequency channel tuned to the mass detection problem. O...

In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of descriptors, based on Shapelet decomposition, derive the source images that generate the observed ROS in mammograms. The Shapelet decomposition coefficients can be used efficiently to detect ROS areas using Support-Vector-Machines (SV...

This paper presents an approach for detecting microcalcifications in digital mammograms. The microcalcifications appear i n small clusters of few pixels with relatively high intensity compared with their neighboring pixels. The processing scheme used here focuses on detection of microcalcification in a very weak contrast to their background and presents a computerized technique to identify the ...

An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram s...

Clusters of Microcalcifications which appear like small white grains of sand on Mammograms are the earliest signs of Breast Cancer. In this work we employ a Gabor filter bank for texture analysis of mammograms to detect microcalcifications. A subset of the Gabor filter bank with a certain central frequency and different orientations is used to obtain the Gabor-filtered images. The filtered imag...

Basics of Bayesian statistics in inverse problems using the maximum entropy principle are summarized in connection with the restoration of positive, additive images from various types of data like X-ray digital mammograms. An efficient iterative algorithm for image restoration from large data sets based on the conjugate gradient method and Lagrange multipliers in nonlinear optimization of a spe...

The amount of breast compression applied during a mammographic exam affects the appearance of mammograms by introducing variations in the shape, position, and contrast of breast anatomical structures, which can conceal existing breast abnormalities or generate false alarms. Due to the complex tissue organization and elastic properties of the breast and the projective nature of mammography, rigi...

Breast cancer throughout the world is a significant health problem for women. Small clusters of microcalcifications appearing as collection of white spots on mammograms indicate an early warning of breast cancer. In present work we have initiated computer-aided analysis of mammograms to automate the diagnostic procedures for breast cancer screening using multiresolution and FCM based clustering...