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http://dx.doi.org/10.6109/jkiice.2011.15.7.1573

Recognition for Lung Cancer using PCA in the Digital Chest Radiography  

Park, Hyung-Hu (고신대학교대학원)
Ok, Chi-Sang (고신대학교대학원 보건과학과)
Kang, Se-Sik (부산가톨릭대학교방사선과)
Ko, Sung-Jin (부산가톨릭대학교방사선과)
Choi, Seok-Yoon (부산가톨릭대학교방사선과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.15, no.7, 2011 , pp. 1573-1582 More about this Journal
Abstract
Risk of lung cancer among lung-related diseases has gradually increased during last decades. The chest digital radiography is the primary diagnosis method for lung cancer. Diagnosing lung cancer using this method requires doctors of ripe experience. Despite their experience there are often wrong diagnoses, which decrease early diagnosis and survival rates of patients. The aim of this study was intended to establish the base on the Computer Aided Diagnosis (CAD) by analyzing Image Recognition Algorithm using Principle component Analysis (PCA) and diagnosing patient's chest X-ray image. The database obtained through this approach enables a doctor to significantly reduce misdiagnosis during the early diagnosis stage, if he or she utilizes it as the preliminary reading step. Case studies were carried out using normal organ, and organs suffering from bronchogenic carcinoma and granuloma. A normal image and unique disease images were extracted after PCA analysis, and their cross-recognition efficiency were compared each other. The result revealed that the recognition rate was much high between normal and disease images, but relatively low between two disease images. In order to increase the recognition efficiency among chest diseases the related algorithms have to be developed continuously in the future study, and such effort will establish the resolute base for CAD.
Keywords
PCA; Image Recognition; Lung cancer; Bronchogenic Carcinoma; Granuloma;
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  • Reference
1 W R .Hendee, et. al, "The information perspective of radiology", AJR pp. 1387-1389, 1992.
2 김진철, "흉부영상에서 컴퓨터지원진단을 위한 간질성 폐질환의 특징분석과 검출", 전남대학교 대학원, pp. 4-7. 2003.
3 이정원, "흉부 CT에서의 폐 분할 알고리즘과 고립 특성을 이용한 폐 결절 자동검출 알고리즘 개발", 서울대학교 대학원, pp. 12-13. 2002.
4 류지원, "단순흉부영상의 유전자 알고리즘을 이용한 폐 결절 자동 추출", 전남대학교 대학원, pp. 5-6, 2003.
5 R. T. Greenlee, T. Nurray, S. Bolden, P. A. Wingo, "Cancer Statistics 2000", CA Cancer J clinic, 50 pp. 7-33, 2000.
6 C. V. Zwirewich, S. Veda, R. Miller, "Solitary Pulmonary Nodule High-Resolution CT and Radiological Pathologic Correlation", Radiology, 179 pp. 469-476, 1991.   DOI
7 D. C. Chan, W. R. Wu, "Image contrast enhancement based on a histogram transformation of local standard deviation", IEEE transactions on Medical imaging, (68);vol.17. no.4 pp. 518-531, 1998.   DOI   ScienceOn
8 임정기 등, "흉부방사선과학", 일조각, pp. 202-283. 2006.
9 김정의, "A study on real-time Face Recognition using PCA", 동신대학교 대학원, pp. 7-32, 2006.
10 K. Doi, H. MacMahon, M. L. Giger, and K. R. hoffman, "Computer Aided Diagnosis in Medical imaging", Elsevier Science B. V, pp. 57-59, 1999.
11 정병수, "PCA와 k-Nearest Neighbor 방법을 이용한 모델 기반형 물체 인식", 전남대학교 대학원, pp.2-3, 2008.
12 R. Chellappa, charles. L. Wilson, and S. Sirohey, "Human and Machine Recognition of Face" A Survey.;Proc. IEEE Vol.83 No 5, pp 704-740, May, 1995.
13 이승철, "동적 링크 구조상에서의 얼굴 인식에 관한 연구", 연세대학교 대학원, pp.1-4, 1999.