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|Title: ||Unsupervised Orthogonal Subspace Projection Approach to MR Image Classification|
|Authors: ||Wang, C.-M.;Chen, Clayton C.-C.;Yang, S.-C.;Chung, P.-C.;Chung, Yi-Nung;Yang, C.-W.;Chang, C.-I.|
|Issue Date: ||2012-07-02T02:06:23Z
|Abstract: ||MR images and remotely sensed images share similar image structures and characteristics because they are acquired remotely as image sequences by spectral channels at different wavelengths. As a result, techniques developed for one may be also applicable to the other. In the past, we have witnessed that some techniques that were developed for magnetic resonance imaging (MRI) found great success in remote sensing image applications. Unfortunately, the opposite direction is yet to be investigated. In this paper, we present an application of one successful remote sensing image classification technique, called orthogonal subspace projection (OSP), to magnetic resonance image classification.|
Unlike classical image classification techniques, which are designed on a pure pixel basis, OSP is a mixed pixel classification
technique that models an image pixel as a linear mixture of different material substances assumed to be present in the image data, then estimates
the abundance fraction of each individual material substance within a pixel for classification. Technically, such mixed pixel classification
is performed by estimating the abundance fractions of material substances resident in a pixel, rather than assigning a class label to it as
usually done in pure-pixel-based classification techniques such as a minimum-distance or nearest-neighbor rule. The advantage of mixed
pixel classification has been demonstrated in many applications in remote sensing image processing. The MRI experiments reported in this
paper further show that mixed pixel classification may have advantages over the pure pixel classification.
|Relation: ||OpticalEngineering, 41(7): 1546-1557|
|Appears in Collections:||[電機工程學系] 期刊論文|
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