Classification of anatomical structures in MR brain images using fuzzy parameters

IEEE Transactions on Bio-medical Engineering
Maria-Elena Algorri, Fernando Flores-Mangas

Abstract

We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses low-level image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use single-echo MR images because of their high volume...Continue Reading

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Citations

May 5, 2011·Surgical Neurology International·Saniya Siraj GodilUvais Qidwai
Aug 28, 2009·Journal of Magnetic Resonance Imaging : JMRI·Todd R Jensen, Kathleen M Schmainda
Nov 11, 2016·IEEE Transactions on Bio-medical Engineering·He DengXin Zhou

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