Evaluation of Bayesian network to classify clustered microcalcifications

Ana C. Patrocinio; Homero Schiabel; Roseli Ap. F. Romero

doi:10.1117/12.535757

12 May 2004 Evaluation of Bayesian network to classify clustered microcalcifications

Ana C. Patrocinio, Homero Schiabel, Roseli Ap. F. Romero

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535757
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

The purpose of this work is the evaluation and analysis of Bayesian network models in order to classify clusters of microcalcifications to supply a second opinion to the specialists in the detection of breast diseases by mammography. Bayesian networks are statistics techniques, which provide explanation about the inferences and influences among features and classes of a determinated problem. Therefore, the technique investigation will aid in obtaining more detailed information to the diagnosis in a CAD scheme. From regions of interest (ROI), containing clusters of microcalcifications, detailed image analysis, pixel to pixel; in this step shape using geometric descriptors (Hu Invariant Moments, second and third order moments and radius gyration); irregularity measure; compactness; area and perimeter extracted descriptors. By using software of Bayesian network models construction, different Bayesian network classifier models could be generated, using the extracted features mentioned above in order to verify their behavior and probabilistic influences and used as the input to Bayesian network, some tests were performed in order to build the classifier. The results of generated nets models validation correspond to an average of 10 tests made with 6 different database sub-groups. The first results of validation have shown 83.17% of correct results.

Citation Download Citation

Ana C. Patrocinio, Homero Schiabel, and Roseli Ap. F. Romero "Evaluation of Bayesian network to classify clustered microcalcifications", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535757

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