Unsupervised orthogonal subspace projection approach to magnetic resonance image classification

Abstract

MR images and remotely sensed images share similar imagestructures and characteristics because they are acquired remotely asimage sequences by spectral channels at different wavelengths. As aresult, 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 isyet to be investigated. In this paper, we present an application of onesuccessful 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 classificationtechnique that models an image pixel as a linear mixture of differentmaterial substances assumed to be present in the image data, then estimates the abundance fraction of each individual material substancewithin 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 asusually 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.