2012 Fiscal Year Final Research Report
Development of manifold learning based texture analysis for lesions within noise included medical images
| Project/Area Number |
23700190
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2012
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| Keywords | 画像情報処理 / 多様体学習 |
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| Research Abstract |
Manifold learning based image analyses for detecting lesion in medical images were studied experimentally. Various medical images are noisy in common, because imaging conditions and parameters are fixed for reduction of patients’ burdens. However some experimental validation showed the noise included medical images has only a limited effect on learning low-dimensional manifold of voxel-wise texture feature space and voxel classification by the texture features. In addition, the benefit of the cascade classification by a manifold based one class classifier and a two class classifier was shown through experiments of the GGO voxel classification and the GGO nodule region detection.
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