Regional Context-Sensitive Support Vector Machine Classifier to Improve Automated Identification of Regional Patterns of Diffuse Interstitial Lung Disease.
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 in...
| Published in: | Journal of Digital Imaging Vol. 24; no. 6; pp. 1133 - 1141 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2011
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104596135&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104596135 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2011 vid: 24 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104596135 67104992 10.1007/s10278-011-9367-0 NLM21311944 104596135 ppf: 1133 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Regional Context-Sensitive Support Vector Machine Classifier to Improve Automated Identification of Regional Patterns of Diffuse Interstitial Lung Disease. aug: au: Lim, Jonghyuck Kim, Namkug Seo, Joon Lee, Young Lee, Youngjoo Kang, Suk-Ho affil: Department of Industrial Engineering, Engineering College, Seoul National University, Seoul South Korea sug: subj: Tomography, X-Ray Computed Diagnosis, Computer Assisted Image Processing, Computer Assisted Lung Diseases, Interstitial Diagnosis Human Automation Validation Studies Comparative Studies T-Tests P-Value Funding Source ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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