Fast density-based lesion detection in dermoscopy images

Computerized Medical Imaging and Graphics

Volumn 35, Issue 2, 2011, Pages 128-136

  Mete, Mutlu ^a   Kockara, Sinan ^b   Aydin, Kemal ^c  

^a TEXAS A AND M UNIVERSITY COMMERCE   (United States)

^b UNIVERSITY OF CENTRAL ARKANSAS   (United States)

^c UNIVERSITY OF ARKANSAS AT PINE BLUFF   (United States)

Author keywords

CAD; Density based clustering; Dermoscopy; Image understanding

Indexed keywords

AUTOMATED ASSESSMENT; AUTOMATED DETECTION; CAD; DATA SETS; DENSITY-BASED; DENSITY-BASED CLUSTERING; DENSITY-BASED CLUSTERING ALGORITHMS; DERMOSCOPY; DERMOSCOPY IMAGES; F-MEASURE; GROUND TRUTH; IMAGING MODALITY; LESION DETECTION; PIGMENTED SKIN LESIONS; RESEARCH FIELDS; SIMPLE POLYGON; SKIN LESION;

DERMATOLOGY; DIAGNOSIS; IMAGE UNDERSTANDING;

CLUSTERING ALGORITHMS;

ARTICLE; DIAGNOSTIC ACCURACY; EPILUMINESCENCE MICROSCOPY; IMAGE ANALYSIS; IMAGE DISPLAY; MELANOMA; PHYSICIAN; PIGMENT DISORDER; PRIORITY JOURNAL; SKIN DISEASE;

ALGORITHMS; DERMOSCOPY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MELANOMA; OBSERVER VARIATION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SKIN NEOPLASMS;

EID: 79951516399     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2010.07.007     Document Type: Article

References (26)

1. American Cancer Society. Cancer Facts & Figures, Available at; 2010 [Accessed 26.06.10]. http://www.cancer.org/acs/groups/content/@nho/documents/document/acspc-024113.pdf.
2. Jemal A., Siegel R., Ward E., Hao Y., Xu J., Murray T., et al. Cancer statistics. A Cancer Journal for Clinicians 2008, 59:225-249. CA.
3. Argenziano G., Soyer H.P., De Giorgi V. Dermoscopy: a tutorial 2002, EDRA Medical Publishing & New Media.
4. Steiner K., Binder M., Schemper M., Wolff K., Pehamberger H. Statistical evaluation of epiluminescence dermoscopy criteria for melanocytic pigmented lesions. Journal of the American Academy of Dermatology 1993, 29(4):581-588.
5. Binder M., Schwarz M., Winkler A., Steiner A., Kaider A., Wolff K., et al. Microscopy: a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Archives Of Dermatology 1995, 131(3):286-291.
6. Fleming M.G., Steger C., Zhang J., Gao J., Cognetta A.B., Pollak I., et al. Techniques for a structural analysis of dermatoscopic imagery. Computerized Medical Imaging and Graphics 1998, 22(5):375-389.
7. Celebi M.E., Aslandogan Y.A., Stoecker W.V., Iyatomi H., Oka H., Chen X. Unsupervised border detection in dermoscopy images. Skin Research and Technology 2007, 13(4):454-462.
8. Celebi M.E., Iyatomi H., Schaefer G., Stoecker W.V. Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics 2009, 33(2):148-153.
9. Hance G.A., Umbaugh S.E., Moss R.H., Stoecker W.V. Unsupervised color image segmentation with application to skin tumor borders. IEEE Engineering in Medicine and Biology 1996, 15(1):104-111.
10. Carlotto M.J. Histogram analysis using a scale-space approach. IEEE Transaction on Pattern Analysis and Machine Intelligence 1987, 1(9):121-129.
11. Rao B.K., Marghoob A.A., Stolz W., Kopf A.W., Slade J., Wasti Q., et al. Can early malignant melanoma be differentiated from atypical melanocytic nevus by in vivo techniques?. Skin Research and Technology 1997, 3(1):8-14.
12. Gao J., Zhang J., Fleming M.G., Pollak I., Cognetta A.B. Segmentation of dermatoscopic images by stabilized inverse diffusion equations. Proceedings of the IEEE International Conference Image Process 1998, 3:823-827.
13. Dhawan A.P., Sicsu A. Segmentation of images of skin lesions using color and texture information of surface pigmentation. Computerized Medical Imaging and Graphics 1992, 16(3):163-177.
14. Meas-Yedid V., Tilie S., Olivo-Marin J.-C. Color image segmentation based on markov random field clustering for histological image analysis, pattern recognition. International conference on, vol. 1, 16th international conference on pattern recognition (ICPR'02) 2002, 10796.
15. Schmid P. Segmentation of digitized dermoscopic images by two-dimensional color clustering. IEEE Transaction on Medical Imaging 1999, 18:164-171.
16. Lee I., Estivill-Castro V. Polygonization of point clusters through cluster boundary extraction for geographical data mining. Proceedings of the 10th international symposium on spatial data handling 2002, 27-40.
17. Nosovskiy G.V., Liu D., Sourina O. Automatic clustering and boundary detection algorithm based on adaptive influence function. Pattern Recognition 2008, 41(9):2757-2776.
18. Ester M., Kriegel H.-P., Sander J., Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of 2nd international conference on knowledge discovery and data mining 1996, 226-231.
19. Levkowitz H., Herman G.T. A Generalized lightness, hue, and saturation color model. CVGIP: Graphical Models and Image Processing 1993, 55(4):271-285.
20. Ridler T., Calvard S. Picture thresholding using an iterative selection method. IEEE Transactions in Systems, Man and Cybernet 1978, 8:630-632.
21. Ajtai M, Chvatal V, Newborn MM, Szemeredi E. Crossing-free subgraphs. Theory and practice of combinatorics, volume 12 of annals of discrete mathematics and volume 60 of North-Holland mathematics studies, 9-12; 1982.
22. Argenziano G., Soyer H.P., De Giorgi V., Piccolo D., Carli P., Delfino M., et al. Dermoscopy: a tutorial 2002, EDRA Medical Publishing & New Media, Milan, Italy.
23. Andrew A.M. Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters 1979, 9:216-219.
24. Celebi M.E., Kingravi H.A., Iyatomi H., Aslandogan Y.A., Stoecker W.V., Moss R.H., et al. Border detection in dermoscopy images using statistical region merging. Skin Research and Technology 2008, 14(3):347-353.
25. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions in Systems, Man and Cybernets 1979, 9:62-66.
26. Celebi M.E., Iyatomi H., Schaefer G., Stoecker W.V. Approximate lesion localization in dermoscopy images. Skin Research and Technology 2009, 15(3):314-322.