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2 Systems Science

2 2010

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Fuzzy Sugeno integral interpretation with application in histopathology image analysis

Tabakov M., Kwaśnicka H., Możejko K.

Systems Science

2010

Vol. 36, no 4

71-82

In this article, we present the specification of a histopathology image retrieval system, based on Sugeno fuzzy integral, applied to the breast cancer diagnosis. The system proposed can be used as a medical decision support tool. The decision problem under consideration is related to the problem of recognition of histopathology images with respect to the degree of HER2/neu receptor overexpression. The proposed solution is based on Sugeno fuzzy integral with well-defined interpretation of the problem. Experiments showed usefulness of our approach.

A rule-based region growing fuzzy segmentation system for pathological brain computed tomography images

Tabakov M., Kwaśnicka H., Krynicki K.

Systems Science

2010

Vol. 36, no 2

23-32

In this paper, a new rule-based region growing fuzzy segmentation system, capable to segment computed tomography (CT) grayscale images into physiologically and pathologically meaningful regions for display and measurement, is presented. The proposed segmentation approach uses the Mamdani fuzzy control model and can be considered as a general CT segmentation technique. It can be used as a support tool for recognition of different kinds of brain pathologies. The system considered emulates the complexity of the standard radiological and neurological recognition, by defining appropriate linguistic variables in accordance with a priori introduced fuzzy rule base.