1 August 1990 Cluster analysis of medical magnetic-resonance images data: diagnostic application and evaluation
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We describe the application of statistical clustering algorithms (approximate fuzzy C-means (AFCM) and ISODATA) and a Bayesian/maximum likelihood (BfML) classifier for data dimension reduction and information extraction with MRI. Analyses were performed on 140 cranial and 6 body MR image data sets obtained at 1.5 Tesla (GE Signa) with a variety of pathologies. Cluster analysis methods were run in an unsupervised mode and used to segment image data sets into 32 classes. Unsupervised classification of new image data sets was achieved by training the B/ML classifier on the 32 cluster data set and using the second-order statistics to assign each new image pixel to a cluster centroid in feature space. A translation table was then used to combine these cluster assignments into nine "superclusters" or tissue types. Tissue classification results were evaluated using visual assessment by a radiologic expert and by statistical comparison with a "gold standard" tissue map. Comparison of the newly classified data to the gold standard image using a confusion matrix showed an overall accuracy of 91%. We have found that this approach can improve the diagnostic specificity of MRI and can be applied to new data in an unsupervised mode with a high degree of accuracy.
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Robert L. DeLapaz, Robert L. DeLapaz, Edward Herskovits, Edward Herskovits, Vito Di Gesu, Vito Di Gesu, William J. Hanson, William J. Hanson, Ralph Bernstein, Ralph Bernstein, "Cluster analysis of medical magnetic-resonance images data: diagnostic application and evaluation", Proc. SPIE 1259, Extracting Meaning from Complex Data: Processing, Display, Interaction, (1 August 1990); doi: 10.1117/12.19984; https://doi.org/10.1117/12.19984

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