IEEE Transactions on Pattern Analysis and Machine Intelligence
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns
Artificial Intelligence in Medicine
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Ultrasound is very important imaging modality in diagnosing and monitoring diseases of thyroid gland. We propose using quantifiable indices for texture characterization and classification. Spatial features, co-occurrence texture features, and non-heuristic texture features are compared in this study. The spatial texture features acted as the best descriptors of changed thyroid tissue for texture characterization with a classification success rate of 100%. The co-occurrence features achieved the classification success about 75% and required to be in a group of four or eight features. The results of non-heuristic texture features were on the border between spatial and co-occurrence features. The overall good classification results confirm that the information related to diagnosis can be adequately extracted from sonographic images of subsurface organs. Quantitative indicators enable reproducibility of the sonographic diagnosis, facilitate assessment of changes of the disease and make possible the comparison of different physicians' findings of a sonographic examination.