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Proceedings Paper

Separation of malignant and benign masses using image and segmentation features
Author(s): Lisa M. Kinnard; Shih-Chung Benedict Lo; Paul C. Wang; Matthew T. Freedman; Mohamed F. Chouikha
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Paper 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 Az 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.

Paper Details

Date Published: 15 May 2003
PDF: 8 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481889
Show Author Affiliations
Lisa M. Kinnard, Georgetown Univ. Medical Ctr. (United States)
Howard Univ. (United States)
Shih-Chung Benedict Lo, Georgetown Univ. Medical Ctr. (United States)
Paul C. Wang, Howard Univ. (United States)
Matthew T. Freedman, Georgetown Univ. Medical Ctr. (United States)
Mohamed F. Chouikha, Howard Univ. (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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