Browse > Article

Pulmonary Vessel Extraction and Nodule Reclassification Method Using Chest CT Images  

Kim, Hyun-Soo (Dept. of Electronic Engineering, Inha University)
Peng, Shao-Hu (Dept. of Electronic Engineering, Inha University)
Muzzammil, Khairul (Dept. of Electronic Engineering, Inha University)
Kim, Deok-Hwan (Dept. of Electronic Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.46, no.6, 2009 , pp. 35-43 More about this Journal
Abstract
In the Computer Aided Diagnosis(CAD) System, the efficient way of classifying nodules from chest CT images of a patient is to perform the classification of the remaining part after the pulmonary vessel extraction. During the pulmonary vessel extraction, due to the small difference between the vessel and nodule features in imaging studies such as CT scans after having an injection of contrast, nodule maybe extracted along with the pulmonary vessel. Therefore, the pulmonary vessel extraction method plays an important role in the nodule classification process. In this paper, we propose a nodule reclassification method based on vessel thickness analysis. The proposed method consist of four steps, lung region searching step, vessel extraction and thinning step, vessel topology formation and correction step and the reclassification of nodule in the vessel candidate step. The radiologists helped us to compare the accuracy of the CAD system using the proposed method and the accuracy of general one. Experimental results show that the proposed method can extract pulmonary vessels and reclassify false-positive nodules accurately.
Keywords
Chest CT Image; Computer Aided Diagnosis; Vessel Extraction; Nodule Reclassification;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 C. Kirbas and F. Quek, 'A review of vessel extraction techniques and algorithms', ACM Computing Surveys, 2004
2 임예니, 홍헬렌, '흉부 CT 영상의 밝기값 정보를 사용한 폐 구조물 자동 분할,' 한국정보과학회 논문지:소프트웨어 및 응용, 제33권, 제11호, 2006년 11월
3 N. Otsu, 'A threshold selection method from gray level histograms', IEEE Transaction on Systems, Man and Cybernetics, 1979
4 S. H. Peng, H. S. Kim, D. H. Kim, 'Speeded Up Feature Extraction for CT Image Based on Integral Image Technique', International Forum on Medical Imaging in Asia, 2009
5 황운주, 박성욱, 박종관, 박종욱, '지문 영상의 분해 및 합성에 의한 주름선 검출방법', 대한전자공학회, 전자공학회논문지-CI, 제44권, 제3호, pp. 90-97, 2007년 5월   과학기술학회마을
6 이덕운, 남우현, 나종범, '진단 및 치료를 위한 3차원 의료 영상의 응용', 대한전자공학회 전자공학회지, 제35권, 제4호, pp. 67-75, 2008년 4월   과학기술학회마을
7 R. Plamondon and S. N. Srihari, 'On-line and Off-line Handwriting Recognition : A Comprehensive Survey,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 63–84, January 2000   DOI   ScienceOn
8 K. Palágyi, A. Kuba, 'A parallel 3D 12-subiteration thinning algorithm', Graphical Models and Image Processing, pp. 199-221, 1999   DOI   ScienceOn
9 Y. Yim, H. Hong, J. B. Seo, Y. G. Shin, ,Accurate Extraction of the Lungs and Pulmonary Vessels from Chest CT: Gradient-based Method,, RSNA, 2007
10 A. Teramoto, M. Tsuzaka, T. Hara, H. Fujita, 'High-speed Detection Method of Solitary Nodules in 3D Chest CT images Based on Cylindrical Filter', International Forum on Medical Imaging in Asia, 2009