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30 May 2003 Independent component analysis assisted unsupervised multispectral classification
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The goal of unsupervised multispectral classification is to precisely identify objects in a scene by incorporating the complementary information available in spatially registered multispectral images. If the channels are less noisy and are as statistically independent as possible, the performance of the unsupervised classifier will be better. The discriminatory power of the classifier also increases if the individual channels have good contrast. However, enhancing the contrast of the channels individually does not necessarily produce good results. Hence there is a need to preprocess the channels such that they have a high contrast and are as statistically independent as possible. Independent Component Analysis (ICA) is a signal processing technique that expresses a set of random variables as linear combinations of statistically independent component variables. The estimation of ICA typically involves formulating a cost function which measures nongaussianity/ gaussianity which is subsequently maximized or minimized. The resulting images are maximally statistically independent and have high contrast. Unsupervised classification on these images captures more information than on the original images. In preliminary studies, we were able to classify detailed neuroanatomical structures such as the putamen and choroid plexus, from the independent component channels. These structures could not be delineated from the original images using the same classifier.
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Srinivasan Rajagopalan and Richard A. Robb "Independent component analysis assisted unsupervised multispectral classification", Proc. SPIE 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, (30 May 2003);

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