A novel algorithm for the detection of melanoma border

There is an ongoing research effort to provide computer-aided imaging tools in order to support the diagnosis and early detection of malignant melanomas. The first step towards producing such a system is the automated and accurate boundary detection of skin lesion. Therefore, the present study introduces a new, simple, and very fast algorithm that has the ability to detect effectively and automatically the border of possible melanoma. The complexity of the proposed algorithm is O(√N) and thus the execution time is dramatically minimized.

[1]  H P Soyer,et al.  Internet‐based program for automatic discrimination of dermoscopic images between melanomas and Clark naevi , 2004, The British journal of dermatology.

[2]  W Abmayr,et al.  Improving early recognition of malignant melanomas by digital image analysis in dermatoscopy. , 1997, Studies in health technology and informatics.

[3]  Pavlos I. Lazaridis,et al.  Simple Matlab Tool for Automated Malignant Melanoma Diagnosis , 2007 .

[4]  Robert H. Barbour,et al.  Automated melanoma diagnosis: where are we at? , 2000, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[5]  J. Mayer,et al.  Systematic review of the diagnostic accuracy of dermatoscopy in detecting malignant melanoma , 1997, The Medical journal of Australia.

[6]  R Marchesini,et al.  The ABCD system of melanoma detection , 1999, Cancer.

[7]  L. Naldi,et al.  The epidemiology of skin cancer , 2002, The British journal of dermatology.

[8]  Harald Ganster,et al.  Automated Melanoma Recognition , 2001, IEEE Trans. Medical Imaging.

[9]  M. Martinelli,et al.  Native specific activity of glutathione peroxidase (GPx-1), phospholipid hydroperoxide glutathione peroxidase (PHGPx) and glutathione reductase (GR) does not differ between normo- and hypomotile human sperm samples. , 2005, International journal of andrology.

[10]  R. Johr.  Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist. , 2002, Clinics in dermatology.

[11]  S. Menzies,et al.  Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. , 1996, Archives of dermatology.

[12]  Randy H. Moss,et al.  Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes , 2005, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[13]  Scott E. Umbaugh,et al.  UNSUPERVISED COLOR IMAGE SEGMENTATION , 1996 .

[14]  Masafumi Hagiwara,et al.  Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system , 2006, Melanoma research.

[15]  C Bartoli,et al.  Results obtained by using a computerized image analysis system designed as an aid to diagnosis of cutaneous melanoma , 1992, Melanoma research.

[16]  R. H. Moss,et al.  Digital imaging in dermatology. , 1992, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[17]  E. Claridge,et al.  Computer screening for early detection of melanoma—is there a future? , 1995, The British journal of dermatology.

[18]  Z. Argényi.  Dermoscopy (epiluminescence microscopy) of pigmented skin lesions. Current status and evolving trends. , 1997, Dermatologic clinics.

[19]  A. Green,et al.  Computer image analysis in the diagnosis of melanoma. , 1994, Journal of the American Academy of Dermatology.

[20]  Clement T. Yu,et al.  Segmentation of skin cancer images , 1999, Image Vis. Comput..

[21]  S. Menzies,et al.  Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. , 2003, Archives of dermatology.

[22]  G Rassner,et al.  Clinical and Laboratory Investigations Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions , 2004 .

[23]  W.V. Stoecker,et al.  Boundary detection in skin tumor images: An overall approach and a radial search algorithm , 1990, Pattern Recognit..

[24]  J C Briggs,et al.  Cutaneous melanoma. , 1993, Journal of the American Academy of Dermatology.

[25]  Philippe Schmid-Saugeona,et al.  Towards a computer-aided diagnosis system for pigmented skin lesions. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[26]  W V Stoecker,et al.  Digital imaging in dermatology. , 1992, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[27]  Ilias Maglogiannis,et al.  Computational vision systems for the detection of malignant melanoma. , 2006, Oncology reports.

[28]  H P Soyer,et al.  Dermoscopy of pigmented skin lesions--a valuable tool for early diagnosis of melanoma. , 2001, The Lancet. Oncology.

[29]  O. Ghita,et al.  Segmentation of skin cancer images , 2009 .