An application of integrated clustering to MRI segmentation

doi:10.1016/0167-8655(94)90078-7

Pattern Recognition Letters

Volume 15, Issue 7, July 1994, Pages 731-738

https://doi.org/10.1016/0167-8655(94)90078-7 Get rights and content

Abstract

Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; radiologists have to combine several perceptual information coming from more than one image (usually ⩽4). This to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to perform preliminary segmentation of an image; it can guide the manual classification of body-tissues. Here four clustering techniques and their integration, in an information fusion clustering procedure, are described. The accuracy of the methodology considered is evaluated on real image data. The evaluation is based on the comparison of the results obtained by automatic procedures, with the tissue-classification done by radiologists.

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The collection of various data coming from anatomical and functional imagery is becoming very common for the study of a given pathology, and their aggregation generally allows for a better medical decision in clinical studies. A fusion process is described in this article for the modeling of this aggregation. The process is illustrated in the case of anatomical and functional images of the brain, but the general principle may be extended to other organs. The whole three-step fusion process based on possibilistic logic is detailed and a new class of fusion operator is introduced. The use of fuzziness in the process in general and in the operator in particular allows for the management of uncertainty and imprecision inherent to the images. The fusion process is illustrated in two clinical cases: the study of Alzheimer's disease by MR/SPECT fusion and the study of epilepsy by MR/SPECT/PET fusion. Results are presented and evaluated, and a preliminary clinical validation is achieved. The assessment of the method is encouraging, allowing its application on several clinical problems.
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^☆: Work supported by the Italian Ministry of University and Research under contract MURST 40%, 1993.

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An application of integrated clustering to MRI segmentation☆

Abstract

Nuclear induction

Phys. Rev.

A comparison of clustering algorithms for MRI

IBM-PASC, Tech. Rep. 12-13-89

Clustering algorithms for MRI

The MIDAS Environment