Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

Volumn , Issue , 2012, Pages 97-101

  Mirzaalian, Hengameh ^a   Lee, Tim K ^a,b   Hamarneh, Ghassan ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER ROC CURVE (AUC); CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; COLOR IMAGES; DERMOSCOPY; MACHINE-LEARNING; MALIGNANT MELANOMA; NONINVASIVE METHODS; RADIAL FLUX; SKIN LESION; SUBSURFACE FEATURES;

DERMATOLOGY; DIAGNOSIS; IMAGE ANALYSIS; NONINVASIVE MEDICAL PROCEDURES; ONCOLOGY;

FEATURE EXTRACTION;

EID: 84859909348     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MMBIA.2012.6164758     Document Type: Conference Paper

References (19)

1. American cancer society. Cancer facts and figures. 2009. Atlanta, USA. 57
2. Canadian cancer society's steering committee. Canadian cancer statistics. 2009. Toronto, Canada. 57
3. M. Anantha, R. H. Moss, and W. V. Stoecker. Detection of pigment network in dermatoscopy images using texture analysis. Computerized Medical Imaging and Graphics, 28(5):225-234, 2004. 57
4. G. Argenziano, H. Soyer, V. Giorgio, D. Piccolo, P. Carli, M. D. A. Ferrari, R. Hofmann, D. Massi, G. Mazzocchetti, M. Scalvenzi, and H.Wolf. Interactive Atlas of Dermoscopy. Edra Medical Publishing and New Media, 2000. 57, 58, 60, 61
5. G. Betta, G. D. Leo, G. Fabbrocini, A. Paolillo, and M. Scalvenzi. Automated application of the 7-point checklist diagnosis method for skin lesions: Estimation of chromatic and shape parameters. Instrumentation and Measurement Technology Conference, 3:1818-1822, 2005. 57
6. Y. Boykov and G. Funka-Lea. Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vision, 70(2):109-131, Nov. 2006. 59
7. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE TPAMI, 23:2001, 1999. 59
8. M. E. Celebi, H. Iyatomi, W. V. Stoecker, R. H. Moss, H. S. Rabinovitz, G. Argenziano, and H. P. Soyer. Automatic detection of blue-white veil and related structures in dermoscopy images. Computerized Medical Imaging and Graphics, 32(8):670 - 677, 2008. 57
9. G. Fabbrocini, G. Betta, G. Leo, C. Liguori, A. Paolillo, A. Pietrosanto, P. Sommella, O. Rescigno, S. Cacciapuoti, F. Pastore, V. Vita, I. Mordente, and F. Ayala. Epiluminescence image processing for melanocytic skin lesion diagnosis based on 7-point check-list: A preliminary discussion on three parameters. The Open Dermatology Journal, 4:110-115, 2010. 57, 60, 61
10. A. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever. Multiscale vessel enhancement filtering. MICCAI, pages 130-137, 1998. 58
11. C. Kirbas and F. K. H. Quek. A review of vessel extraction techniques and algorithms. ACMComputing Surveys, 36:81-121, 2004. 57
12. I. Maglogiannis and C. Doukas. Overview of advanced computer vision systems for skin lesions characterization. IEEE TITB, 13(5):721 -733, 2009. 57
13. S. Park and J. Frnkranz. Efficient pairwise classification. In ECML, volume 4701, pages 658-665. Springer, 2007. 59
14. L. Shi, B. Funt, and G. Hamarneh. Quaternion color curvature. In Color Imaging, pages 338-341, 2008. 59
15. Y. Sun, M. Kamel, and Y. Wang. Boosting for learning multiple classes with imbalanced class distribution. In IEEE ICDM, page 592602, 2006. 60, 61
16. G. Surowka and K. Grzesiak-Kopec. Different learning paradigms for the classification of melanoid skin lesions using wavelets. In IEEE EMBS, pages 3136 -3139, 2007. 57, 60, 61
17. T. Tanaka, S. Torii, I. Kabuta, K. Shimizu, M. Tanaka, and H. Oka. Pattern classification of nevus with texture analysis. IEEE EMBS, pages 1459-1462, 2004. 57
18. V. Vapnik. Statistical Learning Theory. Wiley, 1998. 59
19. H. Zhou, M. Chen, and J. M. Rehg. Dermoscopic interest point detector and descriptor. 1:1318-1321, 2009. 57