Abstract
Dermoscopy is the major imaging modality used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer inconsistencies at interpretation of the same image. Automated detection of lesion borders is very important step in dermoscopy image analysis. One of the highest accuracy rates in the automated lesion border detection field is achieved by Fast Density Based Lesion Detection (FDBLD), which is based on density based clustering of pixel-of-interest. In addition to low border detection error, FDBLD removes redundant computations in well-known spatial density based clustering algorithm DBSCAN; thus, in turn it accelerates clustering process considerably. However, FDBLD is designed to run only on binary images; thus, it requires pre-processing step to convert color image in to a binary image. Furthermore, very important color information in dermoscopy images falls into disuse.
In this study, we develop a modified FDBLD by introducing a new distance measure called Normalized Distance. Our method (ND- FDBLD) improves the efficiency of lesion detection by plugging Normalized Distance into FDBLD. This in turn removes dependency of FDBLD to preprocessing step and improves its accuracy. Moreover, developed distance measure helped involve both color and position dependencies in FDBLD. Both FDBLD and ND-FDBLD methods, in experiments, are tested on the same set of dermoscopy images. ND-FDBLD method is compared not only against FDBLD but also against dermatologists’ drawn ground truth lesion border images. Results revealed that new algorithm is more accurate and efficient than FDBLD on 75 % of 100 dermoscopy images. On 23 % of images both our method and FDBLD performed the same accuracy rates. FDBLD had better accuracy than ND-FDBLD only on two images. In parallel to these results, ND-FDBLD generates more accurate results than FDBLD compared to dermatologists’ drawn ground truth images.
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- 1.
This is also called the International Commission on Illumination.
References
AmericanCancerSociety (2010) Cancer facts & figures. Available: http://www.cancer.org/acs/groups/content/@nho/documents/document/acspc-024113.pdf
Jemal A, Siegel R, Xu J, Ward E (2010) Cancer statistics, 2010. CA Cancer J Clin 60:277–300
Rigel DS, Carucci JA (2000) Malignant melanoma: prevention, early detection, and treatment in the 21st century. CA Cancer J Clin 50:215–236
Binder M, Schwarz M, Winkler A, Steiner A, Kaider A, Wolff K, Pehamberger H (1995) Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch Dermatol 131:286–291
Celebi ME, Iyatomi H, Schaefer G, Stoecker WV (2009) Lesion border detection in dermoscopy images. Comput Med Imaging Graph 33:148–153
Emre Celebi M, Alp Aslandogan Y, Stoecker WV, Iyatomi H, Oka H, Chen X (2007) Unsupervised border detection in dermoscopy images. Skin Res Technol 13:454–462
Pratt WK (2007) Digital image processing: PIKS scientific inside, 4th edn. Wiley-Interscience, Hoboken
Celebi ME, Wen Q, Hwang S, Iyatomi H, Schaefer G (2012) Lesion border detection in dermoscopy images using ensembles of thresholding methods. Skin Res Technol (in press)
Gomez DD, Butakoff C, Ersboll BK, Stoecker W (2008) Independent histogram pursuit for segmentation of skin lesions. IEEE Trans Biomed Eng 55:157–161
Celebi ME, Kingravi HA, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies SW (2008) Border detection in dermoscopy images using statistical region merging. Skin Res Technol 14:347–353
Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision, vol 2. PWS, Pacific Grove
Argenziano G, Soyer HP, Chimenti S, Talamini R, Corona R, Sera F, Binder M, Cerroni L, De Rosa G, Ferrara G (2003) Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J Am Acad Dermatol 48:679–693
Nachbar F, Stolz W, Merkle T, Cognetta AB, Vogt T, Landthaler M, Bilek P, Braun-Falco O, Plewig G (1994) The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. J Am Acad Dermatol 30:551–559
Argenziano G, Fabbrocini G, Carli P, De Giorgi V, Sammarco E, Delfino M (1998) Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch Dermatol 134:1563–1570
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD, pp 226–231
Mete M, Kockara S, Aydin K (2011) Fast density-based lesion detection in dermoscopy images. Comput Med Imaging Graph 35:128–136
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66
Sertel O, Kong J, Shimada H, Catalyurek U, Saltz JH, Gurcan MN (2009) Computer-aided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recognit 42:1093–1103
Mete M, Hennings L, Spencer HJ, Topaloglu U (2009) Automatic identification of angiogenesis in double stained images of liver tissue. BMC Bioinform 10(Suppl 11):S13
Andrew A (1979) Another efficient algorithm for convex hulls in two dimensions. Inf Process Lett 9:216–219
Cleland TM (2004) A practical description of the munsell color system and suggestions for its use 1937. Kessinger Publishing, LLC, Baltimore
Hunter RS (1948) Minutes of the thirty-first meeting of the board of directors of the optical society of America, incorporated. J Opt Soc Am 38:651
Hård A, Sivik L (1981) NCS—natural color system: a Swedish standard for color notation. Color Res Appl 6:129–138
Umbaugh SE (2005) Computer imaging: digital image analysis and processing. CRC, Boca Raton
Russ JC (2007) The image processing handbook. CRC, Boca Raton
Mete M, Topaloglu U (2009) Statistical comparison of color model-classifier pairs in hematoxylin and eosin stained histological images. In: Proceedings of the IEEE symposium on computational intelligence in bioinformatics and computational biology, pp 284–291
Celebi ME, Kingravi HA, Celiker F (2010) Fast colour space transformations using minimax approximations. IET Image Process 4:70–80
Hance GA, Umbaugh SE, Moss RH, Stoecker WV (1996) Unsupervised color image segmentation: with application to skin tumor borders. IEEE Eng Med Biol Mag 15:104–111
Schaefer G, Rajab MI, Emre Celebi M, Iyatomi H (2011) Colour and contrast enhancement for improved skin lesion segmentation. Comput Med Imaging Graph 35:99–104
Mete M, Sirakov NM (2010) Lesion detection in demoscopy images with novel density-based and active contour approaches. BMC Bioinform 11:S23
Acknowledgements
This study is partially supported by University Research Council at University of Central Arkansas. Part of this research is funded by Graduate School of TAMU-Commerce.
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Kockara, S., Mete, M., Suer, S. (2013). Color and Spatial Features Integrated Normalized Distance for Density Based Border Detection in Dermoscopy Images. In: Celebi, M., Schaefer, G. (eds) Color Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5389-1_3
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DOI: https://doi.org/10.1007/978-94-007-5389-1_3
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