"Fuzzy Logic in Computer-Aided Breast Cancer Diagnosis: Analysis of Lobulation"

Artificial Intelligence in Medicine,, 1997, No. 11, pp. 75-85.

B. Kovalerchuk, E. Triantaphyllou, J.F. Ruiz, and J. Clayton

This paper illustrates how a fuzzy logic approach can be used to formalize terms in the American College of Radiology (ACR) Breast Imaging Lexicon. In current practice, radiologists make a relatively subjective determination for many terms from the lexicon related to breast cancer diagnosis. Lobulation and microlobulation of nodules are two important features in the ACR lexicon. We offer an approach for formalizing the distinction of these features and also formalize the description of intermediate cases between lobulated and microlobulated masses. In this paper it is shown that fuzzy logic can be an effective tool in dealing with this kind of problem. The proposed formalization creates a basis for the next three steps: (i) extended verification with blinded comparison studies, (ii) the automatic extraction of the related primitives from the image, and (iii) the detection of lobulated and microlobulated masses based on these primitives.

Key Words:
Fuzzy logic, feature formalization, breast cancer, image recognition, neural networks.

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