Employing the Local Radon Transform for Melanoma Segmentation in Dermoscopic Images
DOI: 10.4103/jmss.JMSS_40_17
Abstract
cancer, has grown signifcantly in the world. The most common technique to observe and diagnosis
of such cancer is the use of noninvasive dermoscope lens. Since this approach is based on the expert
ocular inference, early stage of melanoma diagnosis is a diffcult task for dermatologist. The main
purpose of this article is to introduce an effcient algorithm to analyze the dermoscopic images. The
proposed algorithm consists of four stages including converting the image color space from the RGB
to CIE, adjusting the color space by applying the combined histogram equalization and the Otsu
thresholding‑based approach, border extraction of the lesion through the local Radon transform, and
recognizing the melanoma and nonmelanoma lesions employing the ABCD rule. Simulation results
in the designed user‑friendly software package environment confrmed that the proposed algorithm
has the higher quantities of accuracy, sensitivity, and approximation correlation in comparison with
the other state‑of‑the‑art methods. These values are obtained 98.81 (98.92), 94.85 (89.51), and
90.99 (86.06) for melanoma (nonmelanoma) lesions, respectively.
Full Text:
PDFReferences
Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA Cancer J Clin 2008;58:71-96.
Argenziano G, Soyer H, De Giorgi V. Dermoscopy: A Tutorial. Milan, Italy: EDRE Medical Publishing and New Media; 2002.
Steiner A, Binder M, Schemper M, Wolff K, Pehamberger H. Statistical evaluation of epiluminescence microscopy criteria for melanocytic pigmented skin lesions. J Am Acad Dermatol 1993;29:581-8.
Binder M, Schwarz M, Winkler A, Steiner A, Kaider A, Wolff K, et al. Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch Dermatol 1995;131:286-91.
Ramezani M, Karimian A, Moallem P. Automatic detection of malignant melanoma using macroscopic images. J Med Signals Sens 2014;4:281-90.
Emre Celebi M, Alp Aslandogan Y, Stoecker WV, Iyatomi H, Oka H, Chen X, et al. Unsupervised border detection in dermoscopy images. Skin Res Technol 2007;13:454-62.
Fondn I, Serrano C, Acha B. Segmentation of Skin Cancer Images Based on Multistep Region Growing. MVA2007 IAPR Conference on Machine Vision Applications; 2007. p. 339-42.
Amalian B, Fatichah C, Widyanto MR. ABCD feature extraction for melanoma skin cancer diagnosis. In: Proceedings of the 9th International Conference on Advanced Computer Science and Information System; 2009. p. 224-8.
Smaoui N, Bessassi S. A developed system for melanoma diagnosis. Int J Comput Vis Signal Process 2013;3:10-7.
Grana C, Pellacani G, Cucchiara R, Seidenari S. A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions. IEEE Trans Med Imaging 2003;22:959-64.
Ammara M, Al-Jumaily AA. Fuzzy C. Mean thresholding based level set for automated segmentation of skin lesions. J Signal Inf Process 2013;4:66-71.
Rubegni P, Ferrari A, Cevenini G, Piccolo D, Burroni M, Perotti R, et al. Differentiation between pigmented spitz naevus and melanoma by digital dermoscopy and stepwise logistic discriminant analysis. Melanoma Res 2001;11:37-44.
Zhou H, Schaefer G, Celebi ME, Lin F, Liu T. Gradient vector flow with mean shift for skin lesion segmentation. Comput Med Imaging Graph 2011;35:121-7.
Stoecker WV, Wronkiewiecz M, Chowdhury R, Stanley RJ, Xu J, Bangert A, et al. Detection of granularity in dermoscopy images of malignant melanoma using color and texture features. Comput Med Imaging Graph 2011;35:144-7.
Dalila F, Zohra A, Reda K, Hocine C, Segmentation and classification of melanoma and benign skin lesions. Optik (Stuttg) 2017;140:749-61.
Zhang X. Melanoma segmentation based on deep learning. Comput Assist Surg (Abingdon) 2017;22:267-77.
Pennisi A, Bloisi DD, Nardi D, Giampetruzzi AR, Mondino C, Facchiano A, et al. Skin lesion image segmentation using Delaunay triangulation for melanoma detection. Comput Med Imaging Graph 2016;52:89-103.
Ma Z, Tavares JM. A novel approach to segment skin lesions in dermoscopic images based on a deformable model. IEEE J Biomed Health Inform 2016;20:615-23.
Fdhal N, Kyan M, Androutsos D, Sharma A. Color space transformation from RGB to CIELAB using neural networks. In: Muneesawang P, Wu F, Kumazawa I, Roeksabutr A, Liao M, Tang X, editors. Advances in Multimedia Information Processing-PCM 2009: Lecture Notes in Computer Sciences. Vol. 5879. Berlin, Heidelberg: Springer; 2009.
Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. A novel thresholding based algorithm for detection of vertical root fracture in nonendodontically treated premolar teeth. J Med Signals Sens 2016;6:81-90.
Singh RP, Dixit M. Histogram equalization: A strong technique for image enhancement. Int J Signal Process Image Process Pattern Recognit 2015;8:345-52.
Shao G, Li T, Zuo W, Wu S, Liu T. A combinational clustering based method for cDNA microarray image segmentation. PLoS One 2015;10:e0133025.
Bariamis D, Iakovidis DK, Maroulis D. M3G: Maximum margin microarray gridding. BMC Bioinformatics 2010;11:49.
Pourreza R, Banaee T, Pourreza H, Kakhki RD. A radon transform based approach for extraction of blood vessels in conjunctional images. In: Gelbukh A, Morales EF editors. Mexican International Conference on Artificial Intelligence 2008, Lecture Notes in Artificial Intelligence 5317; 2008. p. 948-56.
Stolz W. ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma. Eur J Dermatol 1994;4:521-7.
Amelard R, Glaister J, Wong A, Clausi DA. High-level intuitive features (HLIFs) for intuitive skin lesion description. IEEE Trans Biomed Eng 2015;62:820-31.
Refbacks
- There are currently no refbacks.
https://e-rasaneh.ir/Certificate/22728
ISSN : 2228-7477