Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Neural Networks - 2005 Special issue: IJCNN 2005
The use of receiver operating characteristic curves in biomedical informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
SOM Segmentation of gray scale images for optical recognition
Pattern Recognition Letters
Application of Kohonen network for automatic point correspondence in 2D medical images
Computers in Biology and Medicine
Journal of Signal Processing Systems
A geographical approach to self-organizing maps algorithm applied to image segmentation
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Neural Network Approach for Identification of Selected Brain Perfusion Abnormalities
IMIS '12 Proceedings of the 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
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In this paper we modify the existing image processing schema for computed tomography perfusion (CTP) maps analysis in order to increase its efficiency in detection and classification of perfusion abnormalities by adding multilayer perceptron neural network diagnostic module and new neural network based image processing procedure. The main new contribution of this paper is description of self-organizing Kohonen's map (SOM) architecture that is used in the image processing step. CTP is based on generation of time series images that shows the flow of contrast material in vascular system of the brain. Despite the fact that SOM was previously reported as a reliable tool for time series classification we decided to utilize it as the extension of our existing methodology. We also present methodology of CTP - based diagnostic system for brain stroke diagnosis updated with our latest results and never before presented validation results. Our proposition is consisted of two steps: initial image processing after which potential asymmetry regions between hemispheres are detected and the second step during which position, type and prognostic map for potentially infarcted tissues are generated. We also made comparison of efficiency of our new approach with existing one. The test set for our algorithm validation was consisted of 75 CTP images triplets (one cerebral blood flow, cerebral blood volume and CT in each) from 30 patients (both man and woman) with suspicion of ischemia/stroke. The maps were previously diagnosed by an expert. Forty two CTP maps were described as normal (no abnormalities), 33 maps showed syndromes of ischemic strokes with various severities. Finally over 82% of CTP maps from our test set were rightly segmented and classified.