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Automatic tissue classification by integrating medical expert anatomic ontologies

Abstract : Image segmentation techniques have been widely used in medical image analysis. However, existing methods can not provide exact physical significance of segmented image regions because they are mainly based on basic image features such as grey level and texture without taking into account specialised medical knowledge. However, medical knowledge plays an indispensable role when doctors analyse medical images in their diagnosis. To deal with this problem, many tissue classification systems were developed by incorporating specific medical knowledge. However, these systems strongly depend on specific applications and then can not provide a general structure for integrating medical knowledge in a larger context. To treat medical image recognition in a systematic way, we propose in this paper a general intelligent tissue classification system which combines both FCM (fuzzy C-means) based clustering algorithm and qualitative medical knowledge on geometric properties of different tissues. In this system, a general model is proposed for formalising non structured and non normalised medical knowledge from various medical images. This model utilises a DOGMA approach (a natural language-based ontology system) for formal representation of these geometric features.
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Han Kang, Antonio Pinti, Abdelmalik Taleb-Ahmed. Automatic tissue classification by integrating medical expert anatomic ontologies. International Journal of Advanced Operations Management, Inderscience, 2013, 5 (1), pp.3-13. ⟨10.1504/IJAOM.2013.051322⟩. ⟨hal-03510088⟩



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