Comparative Study of Feature classification Methods for Mass Lesion Recognition in Digitized Mammograms

In this work a comparison of different classification methods for the identification of mass lesions in digitized mammograms is performed. These methods, used in order to develop Computer Aided Detection (CAD) systems, have been implemented in the framework of the MAGIC-5 Collaboration. The system for identification of mass lesions is based on a three-step procedure: a) preprocessing and segmentation, b) region of interest (ROI) searching, c) feature extraction and classification. It was tested on a very large mammographic database (3369 mammographic images from 967 patients). Each ROI is characterized by eight features extracted from a co-occurrence matrix containing spatial statistics inf…

A test to evaluate the impact of the CAD tools in mammographic diagnosis

In this work we present the results of a study about the impact of CAD tools on Sensitivity and Specificity in mammographic diagnosis. The approach is aimed to evaluate the statistical significance through the comparison of these figures of merit obtained in different situations. For this purpose two different CAD tools, the CALMA station (INFN project) and the SecondLook™ station (by CADx) have been used as a support for radiologists.

Dissimilarity Application in Digitized Mammographic Images Classification.

Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 collaboration. In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, an alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) the training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discrim…

Mammogram segmentation by contour searching and massive lesion classification with neural network

The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting massive lesions in mammographic images will be presented. The database consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration. A reduction of the surface under investigation is achieved, without loss of meaningful information, through segmentation of the whole image, by means of a ROI Hunter algorithm. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters. Once the features are computed for each ROI, they …

Dissimilarity Application for Medical Imaging Classification

In the traditional way of learning from examples of objects the classifiers are built in a feature space. However, alternative ways can be found by constructing decision rules on dissimilarity (distance) representations. In such a recognition process a new object is described by its distances to (a subset of) die training samples. The use of the dissimilarities is especially of interest when features are difficult to obtain or when they have a little discriminative power. Purpose of this work is the development of an automatic classification system which could be useful for radiologists in the investigation of breast cancer. The software has been designed in the framework of the MAGIC-5 col…

The MAGIC-5 Project: Medical Applications on a Grid Infrastructure Connection