An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs.
This paper presents an automatic computer-aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping region...
| Publicado en: | Journal of Digital Imaging Vol. 24; no. 3; pp. 382 - 394 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104894859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104894859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2011 vid: 24 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104894859 60502986 10.1007/s10278-010-9276-7 NLM20174852 104894859 ppf: 382 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs. aug: au: Yu, Peichun Xu, Hao Zhu, Ying Yang, Chao Sun, Xiwen Zhao, Jun affil: Department of Biomedical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, NO.800, Dongchuan Road Shanghai 200240 China sug: subj: Diagnosis, Computer Assisted Radiography, Thoracic Pneumoconiosis Diagnosis Radiographic Image Interpretation, Computer-Assisted Methods Human Pneumoconiosis Radiography Algorithms Radiographic Image Enhancement Lung Radiography Evaluation Research Radiography, Computed ROC Curve Funding Source ab: This paper presents an automatic computer-aided detection scheme on digital chest radiographs to detect pneumoconiosis. Firstly, the lung fields are segmented from a digital chest X-ray image by using the active shape model method. Then, the lung fields are subdivided into six non-overlapping regions, according to Chinese diagnosis criteria of pneumoconiosis. The multi-scale difference filter bank is applied to the chest image to enhance the details of the small opacities, and the texture features are calculated from each region of the original and the processed images, respectively. After extracting the most relevant ones from the feature sets, support vector machine classifiers are utilized to separate the samples into the normal and the abnormal sets. Finally, the final classification is performed by the chest-based report-out and the classification probability values of six regions. Experiments are conducted on randomly selected images from our chest database. Both the training and the testing sets have 300 normal and 125 pneumoconiosis cases. In the training phase, training models and weighting factors for each region are derived. We evaluate the scheme using the full feature vectors or the selected feature vectors of the testing set. The results show that the classification performances are high. Compared with the previous methods, our fully automated scheme has a higher accuracy and a more convenient interaction. The scheme is very helpful to mass screening of pneumoconiosis in clinic. 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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