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 language | English |
|---|---|
| Pages (from-to) | 1546-1557 |
| Number of pages | 12 |
| Journal | Optical Engineering |
| Volume | 41 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2002 Jul |
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All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Engineering(all)
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Unsupervised orthogonal subspace projection approach to magnetic resonance image classification. / Wang, Chuin Mu; Chen, Clayton Chi Chang; Yang, Sheng Chi; Chung, Pau Choo; Chung, Yi Nung; Yang, Ching Wen; Chang, Chein I.
In: Optical Engineering, Vol. 41, No. 7, 07.2002, p. 1546-1557.Research output: Contribution to journal › Article
TY - JOUR
T1 - Unsupervised orthogonal subspace projection approach to magnetic resonance image classification
AU - Wang, Chuin Mu
AU - Chen, Clayton Chi Chang
AU - Yang, Sheng Chi
AU - Chung, Pau Choo
AU - Chung, Yi Nung
AU - Yang, Ching Wen
AU - Chang, Chein I.
PY - 2002/7
Y1 - 2002/7
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=0036648641&partnerID=8YFLogxK
U2 - 10.1117/1.1479710
DO - 10.1117/1.1479710
M3 - Article
AN - SCOPUS:0036648641
VL - 41
SP - 1546
EP - 1557
JO - Optical Engineering
JF - Optical Engineering
SN - 0091-3286
IS - 7
ER -