Computer Aided Melanoma Skin Cancer Detection Using Image Processing

Abstract In recent days, skin cancer is seen as one of the most Hazardous form of the Cancers found in Humans. Skin cancer is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most unpredictable. The detection of Melanoma cancer in early stage can be helpful to cure it. Computer vision can play important role in Medical Image Diagnosis and it has been proved by many existing systems. In this paper, we present a computer aided method for the detection of Melanoma Skin Cancer using Image Processing tools. The input to the system is the skin lesion image and then by applying novel image processing techniques, it analyses it to conclude about the presence of skin cancer. The Lesion Image analysis tools checks for the various Melanoma parameters Like Asymmetry, Border, Colour, Diameter,(ABCD) etc. by texture, size and shape analysis for image segmentation and feature stages. The extracted feature parameters are used to classify the image as Normal skin and Melanoma cancer lesion.

[1]  Rahil Garnavi,et al.  Computer-aided diagnosis of melanoma , 2011 .

[2]  M. G. Fleming,et al.  Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. , 2003, Journal of the American Academy of Dermatology.

[3]  Alauddin Bhuiyan,et al.  Automatic Segmentation of Dermoscopy Images Using Histogram Thresholding on Optimal Color Channels , 2011 .

[4]  Ilias Maglogiannis,et al.  Overview of Advanced Computer Vision Systems for Skin Lesions Characterization , 2009, IEEE Transactions on Information Technology in Biomedicine.

[5]  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.

[6]  Z. She,et al.  Combination of features from skin pattern and ABCD analysis for lesion classification , 2007, 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.

[7]  Randy H. Moss,et al.  A methodological approach to the classification of dermoscopy images , 2007, Comput. Medical Imaging Graph..

[8]  K Wolff,et al.  In vivo epiluminescence microscopy: improvement of early diagnosis of melanoma. , 1993, The Journal of investigative dermatology.

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

[10]  Gerald Schaefer,et al.  Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  P. Aegerter,et al.  Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. , 2001, Archives of dermatology.

[13]  Tim K. Lee,et al.  Determining the asymmetry of skin lesion with fuzzy borders , 2005, Comput. Biol. Medicine.