Individual 3D region-of-interest atlas of the human brain: neural-network-based tissue classification with automatic training point extraction

Gudrun Wagenknecht; Hans-Juergen Kaiser; Thorsten Obladen; Osama Sabri; Udalrich Buell

doi:10.1117/12.387693

6 June 2000 Individual 3D region-of-interest atlas of the human brain: neural-network-based tissue classification with automatic training point extraction

Gudrun Wagenknecht, Hans-Juergen Kaiser, Thorsten Obladen, Osama Sabri, Udalrich Buell

Author Affiliations +

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387693
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

The purpose of individual 3D region-of-interest atlas extraction is to automatically define anatomically meaningful regions in 3D MRI images for quantification of functional parameters (PET, SPECT: rMRGlu, rCBF). The first step of atlas extraction is to automatically classify brain tissue types into gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalp/bone (SB) and background (BG). A feed-forward neural network with back-propagation training algorithm is used and compared to other numerical classifiers. It can be trained by a sample from the individual patient data set in question. Classification is done by a 'winner takes all' decision. Automatic extraction of a user-specified number of training points is done in a cross-sectional slice. Background separation is done by simple region growing. The most homogeneous voxels define the region for WM training point extraction (TPE). Non-white-matter and nonbackground regions are analyzed for GM and CSF training points. For SB TPE, the distance from the BG region is one feature. For each class, spatially uniformly distributed training points are extracted by a random generator from these regions. Simulated and real 3D MRI images are analyzed and error rates for TPE and classification calculated. The resulting class images can be analyzed for extraction of anatomical ROIs.

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Gudrun Wagenknecht, Hans-Juergen Kaiser, Thorsten Obladen, Osama Sabri, and Udalrich Buell "Individual 3D region-of-interest atlas of the human brain: neural-network-based tissue classification with automatic training point extraction", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387693

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