Unsupervised orthogonal subspace projection approach to magnetic resonance image classification

Chuin Mu Wang, Clayton Chi Chang Chen, Sheng Chi Yang, Pau Choo Chung, Yi Nung Chung, Ching Wen Yang, Chein I. Chang

Research output: Contribution to journalArticle

21 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1546-1557
Number of pages12
JournalOptical Engineering
Volume41
Issue number7
DOIs
Publication statusPublished - 2002 Jul

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All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

Cite this

Wang, C. M., Chen, C. C. C., Yang, S. C., Chung, P. C., Chung, Y. N., Yang, C. W., & Chang, C. I. (2002). Unsupervised orthogonal subspace projection approach to magnetic resonance image classification. Optical Engineering, 41(7), 1546-1557. https://doi.org/10.1117/1.1479710