Proper Enhancement and Segmentation of the Overexposed Color Skin Cancer Image

Proper enhancement and segmentation of the overexposed color skin cancer images is a great challenging task in medical image processing field. Computer-aided diagnosis (CAD) facilitates quantitative analysis of digital images with a high throughput processing rate. But, analysis of CAD purely depends on the input image quality. Therefore, in this study, overexposed and washed out skin cancer images are enhanced properly with the help of exact hue-saturation-intensity (eHSI) color model and contrast limited adaptive histogram equalization (CLAHE) method which is applied through this model. eHSI color model is hue preserving and gamut problem free. Any gray level image enhancement method can be easily employed for color image through this eHSI model. The segmentation of these enhanced color images has been done by employing one unsupervised clustering approach with the assistance of seven different gray level thresholding methods. Comparison of the segmentation efficiency of gray level thresholding methods has been done in the cases of overexposed as well as for enhanced images. Proper Enhancement and Segmentation of the Overexposed Color Skin Cancer Image

[1]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[2]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[3]  Ruikang K. Wang,et al.  Uniform enhancement of optical micro-angiography images using Rayleigh contrast-limited adaptive histogram equalization. , 2013, Quantitative imaging in medicine and surgery.

[4]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[5]  Chein-I Chang,et al.  Unsupervised approach to color video thresholding , 2004 .

[6]  Jingyu Yang,et al.  Image retrieval based on the texton co-occurrence matrix , 2008, Pattern Recognit..

[7]  P. D. Thouin,et al.  Survey and comparative analysis of entropy and relative entropy thresholding techniques , 2006 .

[8]  Ibrahim Azad,et al.  Image Processing for Skin Cancer Features Extraction , 2013 .

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

[10]  Changhoon Yim,et al.  Quality Assessment of Deblocked Images , 2011, IEEE Transactions on Image Processing.

[11]  Ali El-Zaart,et al.  Skin Images Segmentation , 2010 .

[12]  Robin N. Strickland,et al.  Digital Color Image Enhancement Based On The Saturation Component , 1987 .

[13]  H.-c. Chen,et al.  Visible colour difference-based quantitative evaluation of colour segmentation , 2006 .

[14]  Chein-I Chang,et al.  A relative entropy-based approach to image thresholding , 1994, Pattern Recognit..

[15]  M S Blois,et al.  Clinical characteristics of early cutaneous melanoma , 1980, Cancer.

[16]  Sankar K. Pal,et al.  Entropy: a new definition and its applications , 1991, IEEE Trans. Syst. Man Cybern..

[17]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[18]  Calvin C. Gotlieb,et al.  Texture descriptors based on co-occurrence matrices , 1990, Comput. Vis. Graph. Image Process..

[19]  M C Mihm,et al.  Early recognition of cutaneous melanoma. , 1979, JAMA.

[20]  S. Pal,et al.  Object-background segmentation using new definitions of entropy , 1989 .

[21]  A. Marghoob,et al.  Techniques of cutaneous examination for the detection of skin cancer , 1995, Cancer.

[22]  Ling Li,et al.  Image enhancement based on contrast limited adaptive histogram equalization for 3D images of stereoscopic endoscopy , 2015, 2015 IEEE International Conference on Information and Automation.

[23]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[24]  C. A. Murthy,et al.  Hue-preserving color image enhancement without gamut problem , 2003, IEEE Trans. Image Process..

[25]  Thierry Pun,et al.  Entropic thresholding, a new approach , 1981 .

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

[27]  Neethu M. Sasi,et al.  Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images , 2013 .

[28]  Raúl San José Estépar,et al.  Image Quality Assessment based on Local Variance , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Claude E. Shannon,et al.  The mathematical theory of communication , 1950 .

[30]  J. Cavanaugh.  Unifying the derivations for the Akaike and corrected Akaike information criteria , 1997 .

[31]  Chein-I Chang,et al.  A computer-aided design mammography screening system for detection and classification of microcalcifications , 2000, Int. J. Medical Informatics.

[32]  David J. Marchette,et al.  Recursive dimensionality reduction using Fisher's linear discriminant , 1998, Pattern Recognit..

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

[34]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[35]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.