Unsupervised orthogonal subspace projection approach to magnetic resonance image classification

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 devel- oped for magnetic resonance imaging (MRI) found great success in re- mote 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 or- thogonal subspace projection (OSP), to magnetic resonance image clas- sification. Unlike classical image classification techniques, which are de- signed 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 es- timates the abundance fraction of each individual material substance within a pixel for classification. Technically, such mixed pixel classifica- tion is performed by estimating the abundance fractions of material sub- stances 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 re- mote sensing image processing. The MRI experiments reported in this paper further show that mixed pixel classification may have advantages over the pure pixel classification. © 2002 Society of Photo-Optical Instrumentation