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Proceedings Paper

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine
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Paper Abstract

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343P (3 March 2017); doi: 10.1117/12.2253294
Show Author Affiliations
Donghoon Lee, Research Institute of Health Science, Yonsei Univ. (Korea, Republic of)
Ye-seul Kim, College of Health Science, Yonsei Univ. (Korea, Republic of)
Sunghoon Choi, College of Health Science, Yonsei Univ. (Korea, Republic of)
Haenghwa Lee, College of Health Science, Yonsei Univ. (Korea, Republic of)
Byungdu Jo, College of Health Science, Yonsei Univ. (Korea, Republic of)
Seungyeon Choi, Research Institute of Health Science, Yonsei Univ. (Korea, Republic of)
Jungwook Shin, LISTEM Corp. (Korea, Republic of)
Hee-Joung Kim, Research Institute of Health Science, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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