Separation of malignant and benign masses using image and segmentation features

Lisa M. Kinnard; Shih-Chung Benedict Lo; Paul C. Wang; Matthew T. Freedman M.D.; Mohamed F. Chouikha

doi:10.1117/12.481889

15 May 2003 Separation of malignant and benign masses using image and segmentation features

Lisa M. Kinnard, Shih-Chung Benedict Lo, Paul C. Wang, Matthew T. Freedman M.D., Mohamed F. Chouikha

Author Affiliations +

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481889
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

The purpose of this study is to investigate the efficacy of image features versus likelihood features of tumor boundaries for differentiating benign and malignant tumors and to compare the effectiveness of two neural networks in the classification study: (1) circular processing-based neural network and (2) conventional Multilayer Perceptron (MLP). The segmentation method used is an adaptive region growing technique coupled with a fuzzy shadow approach and maximum likelihood analyzer. Intensity, shape, texture, and likelihood features were calculated for the extracted Region of Interest (ROI). We performed these studies: experiment number 1 utilized image features used as inputs and the MLP for classification, experiment number 2 utilized image features used as inputs and the neural net with circular processing for classification, and experiment number 3 used likelihood values as inputs and the MLP for classification. The experiments were validated using an ROC methodology. We have tested these methods on 51 mammograms using a leave-one-case-out experiment (i.e., Jackknife procedure). The A_z values for the four experiments were as follows: 0.66 in experiment number 1, 0.71 in experiment number 2, and 0.84 in experiment number 3.

Citation Download Citation

Lisa M. Kinnard, Shih-Chung Benedict Lo, Paul C. Wang, Matthew T. Freedman M.D., and Mohamed F. Chouikha "Separation of malignant and benign masses using image and segmentation features", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481889

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