Regional Context-Sensitive Support Vector Machine Classifier to Improve Automated Identification of Regional Patterns of Diffuse Interstitial Lung Disease
- 11 February 2011
- journal article
- research article
- Published by Springer Science and Business Media LLC in Journal of Digital Imaging
- Vol. 24 (6), 1133-1140
- https://doi.org/10.1007/s10278-011-9367-0
Abstract
We propose the use of a context-sensitive support vector machine (csSVM) to enhance the performance of a conventional support vector machine (SVM) for identifying diffuse interstitial lung disease (DILD) in high-resolution computerized tomography (HRCT) images. Nine hundred rectangular regions of interest (ROIs), each 20 × 20 pixels in size and consisting of 150 ROIs representing six regional disease patterns (normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation), were marked by two experienced radiologists using consensus HRCT images of various DILD. Twenty-one textual and shape features were evaluated to characterize the ROIs. The csSVM classified an ROI by simultaneously using the decision value of each class and information from the neighboring ROIs, such as neighboring region feature distances and class differences. Sequential forward-selection was used to select the relevant features. To validate our results, we used 900 ROIs with fivefold cross-validation and 84 whole lung images categorized by a radiologist. The accuracy of the proposed method for ROI and whole lung classification (89.88 ± 0.02%, and 60.30 ± 13.95%, respectively) was significantly higher than that provided by the conventional SVM classifier (87.39 ± 0.02%, and 57.69 ± 13.31%, respectively; paired t test, p < 0.01, and p < 0.01, respectively). We conclude that our csSVM provides better overall quantification of DILD.Keywords
This publication has 26 references indexed in Scilit:
- Performance testing of several classifiers for differentiating obstructive lung diseases based on texture analysis at high-resolution computerized tomography (HRCT)Computer Methods and Programs in Biomedicine, 2009
- Feasibility of Automated Quantification of Regional Disease Patterns Depicted on High-Resolution Computed Tomography in Patients with Various Diffuse Lung DiseasesKorean Journal of Radiology, 2009
- Development of an Automatic Classification System for Differentiation of Obstructive Lung Disease using HRCTJournal of Digital Imaging, 2008
- Texture-Based Quantification of Pulmonary Emphysema on High-Resolution Computed Tomography: Comparison With Density-Based Quantification and Correlation With Pulmonary Function TestInvestigative Radiology, 2008
- Multi-level classification of emphysema in HRCT lung imagesPattern Analysis and Applications, 2007
- Computer-aided Classification of Interstitial Lung Diseases Via MDCT: 3D Adaptive Multiple Feature Method (3D AMFM)Academic Radiology, 2006
- A comparison of methods for multiclass support vector machinesIEEE Transactions on Neural Networks, 2002
- An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing imagesIEEE Transactions on Image Processing, 2002
- Combining belief networks and neural networks for scene segmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
- A statistical approach to the fusion of spectral and spatio-temporal contextual information for the classification of remote-sensing imagesPattern Recognition Letters, 2002