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Classification of Emphysema Subtypes: Comparative Assessment of Local Binary Patterns and Related Texture Features

Advances in Computed Tomography 2015年第3期4卷 47-55页

作者： Mizuho Nishio hisanobu koyama Yoshiharu Ohno Kazuro Sugimura Division of Functional and Diagnostic Imaging Research Department of Radiology Kobe University Graduate School of Medicine Kobe Japan Advanced Biomedical Imaging Research Center Kobe University Graduate School of Medicine Kobe Japan Division of Radiology Department of Radiology Kobe University Graduate School of Medicine Kobe Japan

The purpose of this study was to assess usefulness of local binary patterns (LBP) and related texture features, namely completed local binary patterns (CLBP) and local ternary patterns (LTP), for the classification of emphysema subtypes on low-dose CT images. Fifty patients (34 men and 16 women;age, 67.5 ± 10.1 years) who underwent low-dose CT (60 mAs) were included. They were comprised of 17 never smokers, 13 smokers without COPD, and 20 smokers with COPD. By consensus reading of low-dose CT images from these patients, two radiologists selected 3681 nonoverlapping regions of interest (ROIs) and annotated them as one of the following three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. From these ROIs, histogram of CT densities, LBP, CLBP, and LTP were calculated, and the 3 types of texture histograms were concatenated with the CT density histogram. These 3 types of histograms (referred to as combined LBP, combined CLBP, and combined LTP) were used to classify ROI using linear support vector machine. For each type of the combined histogram, the accuracy of classification was determined by patient-based 10-fold cross validation. The best accuracy of combined LBP, combined CLBP, and combined LTP were 81.36%, 82.99%, and 83.29%, respectively. Compared to the classification accuracies obtained with combined LBP, those with combined LTP or combined CLBP were consistently improved. In conclusion, the results of this study suggest that, on low-dose CT, LTP and CLBP were more useful for the classification of emphysema subtypes than LBP.

关键词： Emphysema COPD Texture Analysis Low-Dose CT

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Tumor Segmentation on <sup>18</sup>F FDG-PET Images Using Graph Cut and Local Spatial Information

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Open Journal of Medical Imaging 2015年第3期5卷 174-181页

作者： Mizuho Nishio Atsushi K. Kono Kazuhiro Kubo hisanobu koyama Tatsuya Nishii Kazuro Sugimura Clinical PET Center Institute of Biomedical Research and Innovation Kobe Japan Division of Functional and Diagnostic Imaging Research Department of Radiology Kobe University Graduate School of Medicine Kobe Japan Division of Radiology Department of Radiology Kobe University Graduate School of Medicine Kobe Japan Center for Radiology and Radiation Oncology Kobe University Hospital Kobe Japan

The purpose of this study was to develop methodology to segment tumors on 18F-fluorodeoxyg- lucose (FDG) positron emission tomography (PET) images. Sixty-four metastatic bone tumors were included. Graph cut was used for tumor segmentation, with segmentation energy divided into unary and pairwise terms. Locally connected conditional random fields (LCRF) were proposed for the pairwise term. In LCRF, three-dimensional cubic window with length L was set for each voxel, and voxels within the window were considered for the pairwise term. Three other types of segmentation were applied: region-growing based on 35%, 40%, and 45% of the tumor maximum standardized uptake value (RG35, RG40, and RG45, respectively), SLIC superpixels (SS), and region-based active contour models (AC). To validate the tumor segmentation accuracy, dice similarity coefficients (DSC) were calculated between the result of each technique and manual segmentation. Differences in DSC were tested using the Wilcoxon signed-rank test. Mean DSCs for LCRF at L = 3, 5, 7, and 9 were 0.784, 0.801, 0.809, and 0.812, respectively. Mean DSCs for the other techniques were: RG35, 0.633;RG40, 0.675;RG45, 0.689;SS, 0.709;and AC, 0.758. The DSC differences between LCRF and other techniques were statistically significant (p < 0.05). Tumor segmentation was reliably performed with LCRF.

关键词： Image Segmentation FDG-PET Graph Cut

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