0000000000628652

AUTHOR

M. Glorioso

Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences

Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be p…

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A method to reduce the FP/imm number through CC and MLO views comparison in mammographic images

In this paper we propose a method to reduce the FP/imm number through CC and MLO mammographic views comparison of the same patient. The proposed solution uses the symmetry properties of the breast to compute a geometric transformation that permits to represent the two images in comparable coordinates systems. Through this method, potential pathological ROIs of one of the projections are correlated with the ROIs in the second view. To show the effectiveness of the result we apply the method on a dataset composed of 112 couples of pathological images. Experiments shows that method enables a reduction by up to 700/0 of the FP/imm number detected after the classification step

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