The objective of this work is to provide a probability of malignancy of a mammographic mass to the interpreting physician. Using the location of a mass, it is automatically segmented using fuzzy clustering. Features are extracted from the segmentation results using morphological, first-order statistical, and texture measures. Selection of relevant features is done using sequential selection. Fitness functions are based on the scatter matrices, k-nearest neighbors classifier, or neural network classifier using two-fold cross validation. The diagnosis is then provided by a trained three layer neural network. Feature selection provides a dramatic reduction in the number of required measurements to less than 25 as well as improve the accuracy of the results, from about 70% correct to 82% correct. The area under the ROC curve also increased dramatically. Computer vision applied to mammographic masses results in a very complex data space, that requires careful analysis for the design of a classifier. While further improvements are needed, current results are becoming clinically interesting.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Computer diagnosis of mammographic masses


    Contributors:


    Publication date :

    2000-01-01


    Size :

    736390 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Computer Diagnosis of Mammographic Masses

    Velthuizen, R. | British Library Conference Proceedings | 2000


    A novel approach to computer-aided diagnosis of mammographic images

    Sari-Sarraf, H. / Gleason, S.S. / Hudson, K.T. et al. | IEEE | 1996


    A Novel Approach to Computer-Aided Diagnosis of Mammographic Images

    Sari-Sarraf, H. / Gleason, S. S. / Hudson, K. T. et al. | British Library Conference Proceedings | 1996


    A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection

    Suzuki, Shintaro / Zhang, Xiaoyong / Homma, Noriyasu et al. | British Library Online Contents | 2018


    Hierarchical, Multi-Resolution Models for Object Recognition: Applications to Mammographic Computer-Aided Diagnosis

    Sajda, P. / Spence, C. / Parra, L. et al. | British Library Conference Proceedings | 2000