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- Author: Mehmet Kale
- Publisher: VDM Verlag
- ISBN-10: 3639132483
- ISBN-13: 9783639132489
- Format: 15.2 x 22.9 x 0.8 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Multispectral Co-occurrence Analysis for Medical Image Processing (e-book) (used book) | bookbook.eu
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Description
Presented is a new computer-aided multispectral image processing method which is used in 3 spatial dimensions and 1 spectral dimension where the dynamic, contrast enhanced magnetic resonance parameter maps derived from voxelwise model-fitting represent the spectral dimension. The method is based on co-occurrence analysis using a 3-dimensional window of observation which introduces an automated identification of suspicious lesions. The co-occurrence analysis defines 21 different statistical features, a subset of which were input to a neural network classifier where the assessments of the voxelwise majority of a group of radiologist readings were used as the gold standard. The voxelwise true positive fraction (TPF) and false positive fraction (FPF) results of the computer classifier were statistically indistinguishable from the TPF and FPF results of the readers using a one sample paired t-test. In order to observe the generality of the method, two different groups of studies were used with widely different image acquisition specifications.
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- Author: Mehmet Kale
- Publisher: VDM Verlag
- ISBN-10: 3639132483
- ISBN-13: 9783639132489
- Format: 15.2 x 22.9 x 0.8 cm, softcover
- Language: English English
Presented is a new computer-aided multispectral image processing method which is used in 3 spatial dimensions and 1 spectral dimension where the dynamic, contrast enhanced magnetic resonance parameter maps derived from voxelwise model-fitting represent the spectral dimension. The method is based on co-occurrence analysis using a 3-dimensional window of observation which introduces an automated identification of suspicious lesions. The co-occurrence analysis defines 21 different statistical features, a subset of which were input to a neural network classifier where the assessments of the voxelwise majority of a group of radiologist readings were used as the gold standard. The voxelwise true positive fraction (TPF) and false positive fraction (FPF) results of the computer classifier were statistically indistinguishable from the TPF and FPF results of the readers using a one sample paired t-test. In order to observe the generality of the method, two different groups of studies were used with widely different image acquisition specifications.
Reviews