Multiple network CGP for the classification of mammograms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5484 LNCS, Issue , 2009, Pages 405-413

  Völk, Katharina ^a   Miller, Julian F ^a   Smith, Stephen L ^a  

^a UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Cartesian genetic programming; Evolutionary algorithms; Mammography

Indexed keywords

CARTESIAN GENETIC PROGRAMMING; HIGH RESOLUTION; LAWRENCE LIVERMORE NATIONAL LABORATORIES; MULTIPLE NETWORKS;

COMPUTER SCIENCE; GENETIC PROGRAMMING;

GENETIC ALGORITHMS;

EID: 67650667756     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01129-0_45     Document Type: Conference Paper

References (14)

1. Office for National Statistics, http://www.statistics.gov.uk/cci/nugget. asp?id=575
2. Kopans, D.B.: Lippincott Williams & Wilkins (2006)
3. Gonzalez, R., Woods, R.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)
4. Kim, J., Park, H.: Statistical Textural Features for Detection of Microcalcifications in Digitized Mammograms. IEEE Transactions Medical Imaging 18(3), 231-238 (1999)
5. Fu, J.C., Lee, S.K., Wong, S.T.C., Yeh, J.Y., Wang, A.H., Wu, H.K.: Image segmentation feature selection and pattern classification for mammographic microcalcifications. Computerized Medical Imaging and Graphics 29, 419-429 (2005) (Pubitemid 41247787)
6. Gavrielides, M., Lo, J., Vargas-Voracek, R., Floyd, C.: Segmentation of suspicious clustered microcalcifications in mammograms. Medical Physics 27 (2000) (Pubitemid 30051906)
7. Andolina, V., Lille, S., Willison, K.: Mammographic Imaging: A Practical Guide. Lippincott-Raven (1993)
8. Chan, H., Sahiner, B., Lam, K., Petrick, N., Helvie, M., Goodsitt, M., Adler, D.: Computerized analysis of mammographic microcalcifications in morphological and texture feature space. Medical Physics 25(10) (1998) (Pubitemid 28178360)
9. Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 36, 2967-2991 (2003) (Pubitemid 37293407)
10. Cai, X., Smith, S.L., Tyrrell, A.M.: Benefits of Employing an Implicit Context Representation on Hardware Geometry of CGP. In: Moreno, J.M., Madrenas, J., Cosp, J. (eds.) ICES 2005. LNCS, vol.3637, pp. 143-154. Springer, Heidelberg (2005)
11. Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol.1802, pp. 121-132. Springer, Heidelberg (2000)
12. Walker, J., Walker, J.A., Miller, J.F., Cavill, R.: A Multi-chromosome Approach to Standard and Embedded Cartesian Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 903-910. ACM Press, New York (2006) (Pubitemid 44611420)
13. Lawrence Livermore National Laboratory database, https://www.llnl.gov/
14. Miller, J.F., Smith, S.L.: Redundancy and Computational Efficiency in Cartesian Genetic Programming. IEEE Transactions on Evolutionary Computation 10, 167-174 (2006)