메뉴 건너뛰기




Volumn 5031, Issue , 2003, Pages 102-110

Feature selection for computer-aided polyp detection using genetic algorithms

Author keywords

Computer aided diagnosis; Feature selection; Forward stepwise search; Genetic algorithms; Support vector machines; Virtual colonoscopy

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTERIZED TOMOGRAPHY; FEATURE EXTRACTION; GENETIC ALGORITHMS; VECTOR QUANTIZATION; VIRTUAL REALITY;

EID: 0041780746     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.485796     Document Type: Conference Paper
Times cited : (33)

References (20)
  • 1
    • 0036828526 scopus 로고    scopus 로고
    • Colonic polyps: Complementary role of computer-aided detection in CT colonography
    • R.M. Summers, A. Jerebko, M. Franaszek, J. Malley, and C.D. Johnson, Colonic Polyps: Complementary Role of Computer-aided Detection in CT Colonography. Radiology, 2002. 225:391-399.
    • (2002) Radiology , vol.225 , pp. 391-399
    • Summers, R.M.1    Jerebko, A.2    Franaszek, M.3    Malley, J.4    Johnson, C.D.5
  • 2
    • 0003684449 scopus 로고    scopus 로고
    • The elements of statistical learning: Data mining, inference and prediction
    • New York; Springer
    • T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York; Springer; 2001.
    • (2001)
    • Hastie, T.1    Tibshirani, R.2    Friedman, J.3
  • 3
    • 0003798635 scopus 로고    scopus 로고
    • Support vector machines, and other kernel-based learning methods
    • Cambridge University Press
    • N. Cristianini, and J. Shawe-Taylor, Support Vector Machines, and other Kernel-Based Learning Methods. Cambridge University Press; 2000.
    • (2000)
    • Cristianini, N.1    Shawe-Taylor, J.2
  • 4
    • 0003798627 scopus 로고    scopus 로고
    • Advances in kernel methods; support vector learning
    • B. Schölkopf, C. Burges, AJ Smola (eds); MIT Press
    • B. Schölkopf, C. Burges, AJ Smola (eds), Advances in Kernel Methods; Support Vector Learning. MIT Press; 1999.
    • (1999)
  • 5
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi, and G. John, Wrappers for Feature Subset Selection. Artificial Intelligence, 1997. 97:273-324.
    • (1997) Artificial Intelligence , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.2
  • 7
    • 84900362093 scopus 로고    scopus 로고
    • Feature subset selection by estimation of distribution algorithms
    • E. Cantu-Paz, Feature Subset Selection by Estimation of Distribution Algorithms.
    • Cantu-Paz, E.1
  • 9
    • 0026839971 scopus 로고
    • Fast genetic selection of features for neural network classifiers
    • F. Brill, D. Brown, and W. Martin, Fast Genetic Selection of Features for Neural Network Classifiers. IEEE Transactions on Neural Networks, 1992. 3(2):324-328.
    • (1992) IEEE Transactions on Neural Networks , vol.3 , Issue.2 , pp. 324-328
    • Brill, F.1    Brown, D.2    Martin, W.3
  • 12
    • 0036832996 scopus 로고    scopus 로고
    • Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
    • S. Ho, C. Liu, and S. Liu, Design of an Optimal Nearest Neighbor Classifier using an Intelligent Genetic Algorithm. Pattern Recognition Letter, 2002. 23(13): 1495-1503.
    • (2002) Pattern Recognition Letter , vol.23 , Issue.13 , pp. 1495-1503
    • Ho, S.1    Liu, C.2    Liu, S.3
  • 15
    • 0036740201 scopus 로고    scopus 로고
    • Fast orthogonal forward selection algorithm for feature subset selection
    • K.Z. Mao, Fast Orthogonal Forward Selection Algorithm for Feature Subset Selection. IEEE Transactions on Neural Networks, 2002. 13(5): 1218-1224.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.5 , pp. 1218-1224
    • Mao, K.Z.1
  • 16
    • 0004169853 scopus 로고
    • A comparison of some error estimates for neural network models
    • Technical Report, Stanford University Department of Statistics
    • R. Tibshirani, A comparison of some error estimates for neural network models. Technical Report, Stanford University Department of Statistics; 1995.
    • (1995)
    • Tibshirani, R.1
  • 17
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • T.G. Dietterich, Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10 (7), 1895-1924; 1998.
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1895-1924
    • Dietterich, T.G.1
  • 18
    • 0031105969 scopus 로고    scopus 로고
    • Feature subset selection for classification of histological images
    • J. Jelonek, Jerzy S., Feature Subset Selection for Classification of Histological Images. Artificial Intelligence in Medicine, 1997. 9:22-239.
    • (1997) Artificial Intelligence in Medicine , vol.9 , pp. 22-239
    • Jelonek, J.1    Jerzy, S.2
  • 19
    • 0033863591 scopus 로고    scopus 로고
    • Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size
    • B. Sahiner, H.P. Chan, N. Petrick, R.F. Wagner, and L. Hadjiiski, Feature Selection and Classifier Performance in Computer-Aided Diagnosis: The Effect of Finite Sample Size. Medical Physics, 2000. 27(7): 1509-1522.
    • (2000) Medical Physics , vol.27 , Issue.7 , pp. 1509-1522
    • Sahiner, B.1    Chan, H.P.2    Petrick, N.3    Wagner, R.F.4    Hadjiiski, L.5
  • 20
    • 0041640971 scopus 로고    scopus 로고
    • Multi-network classification scheme for detection of colonic polyps in CT colonography data sets
    • In press: Academic Radiology
    • A.K. Jerebko, JD Malley, M Franaszek, RM Summers, Multi-network classification scheme for detection of colonic polyps in CT colonography data sets. In press: Academic Radiology; 2003.
    • (2003)
    • Jerebko, A.K.1    Malley, J.D.2    Franaszek, M.3    Summers, R.M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.