REVIEW ON EARLY DETECTION OF MELANOMA IN SITU

Skin cancer is the fifth most common type in the united state. It has become a major health issue in the world over the past 40 years and its incidence has increased in recent years. Early detection is an efficient way to diagnose and manage skin cancer. Detection at the melanoma in situ (MIS) stage provides the highest cure rate for melanoma. The ultimate aim of this paper is to provide an overview of statistics and the result of the most important implementation that exist in the literature, while it compare the performance of different classification on pigmented skin lesion (PSL) and also focusing on several segmentations and feature extraction methods. Index Term:- skin cancer, melanoma, melanoma in situ (MIS), pigmented skin lesion (PSL) , segmentation, feature extraction, classification.

[1]  Lars Kai Hansen,et al.  Outlier estimation and detection application to skin lesion classification , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  R. H. Moss,et al.  Neural network diagnosis of malignant melanoma from color images , 1994, IEEE Transactions on Biomedical Engineering.

[3]  R Marchesini,et al.  The invisible colours of melanoma. A telespectrophotometric diagnostic approach on pigmented skin lesions. , 1996, European journal of cancer.

[4]  P. Schmid.  Segmentation of digitized dermatoscopic images by two-dimensional color clustering , 1999, IEEE Transactions on Medical Imaging.

[5]  T. Tanaka,et al.  Feature of malignant melanoma based on color information , 2004, SICE 2004 Annual Conference.

[6]  Eric Lefevre,et al.  Knowledge modeling methods in the framework of evidence theory: an experimental comparison for melanoma detection , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[7]  Atam P. Dhawan,et al.  Depth-Dependent Hemoglobin Analysis From Multispectral Transillumination Images , 2010, IEEE Transactions on Biomedical Engineering.

[8]  Susan M Swetter,et al.  Evaluation of digital dermoscopy in a pigmented lesion clinic: clinician versus computer assessment of malignancy risk. , 2007, Journal of the American Academy of Dermatology.

[9]  B W Chwirot,et al.  Detection of melanomas by digital imaging of spectrally resolved ultraviolet light-induced autofluorescence of human skin. , 1998, European journal of cancer.

[10]  M. Nischik,et al.  Analysis of skin erythema using true-color images , 1997, IEEE Transactions on Medical Imaging.

[11]  Thilo Gambichler,et al.  Characterization of benign and malignant melanocytic skin lesions using optical coherence tomography in vivo. , 2007, Journal of the American Academy of Dermatology.

[12]  A. Venot,et al.  Assessment of healing kinetics through true color image processing , 1993, IEEE Trans. Medical Imaging.

[13]  Lars Kai Hansen,et al.  Detection of skin cancer by classification of Raman spectra , 2004, IEEE Transactions on Biomedical Engineering.

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

[15]  Rita Cucchiara,et al.  A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions , 2003, IEEE Transactions on Medical Imaging.

[16]  Mrinal K. Mandal,et al.  Automated Segmentation of the Melanocytes in Skin Histopathological Images , 2013, IEEE Journal of Biomedical and Health Informatics.

[17]  Ela Claridge,et al.  From Colour to Tissue Histology: Physics Based Interpretation of Images of Pigmented Skin Lesions , 2002, MICCAI.

[18]  Randy H. Moss,et al.  Fast and accurate border detection in dermoscopy images using statistical region merging , 2007, SPIE Medical Imaging.

[19]  Atam P. Dhawan,et al.  Classification of melanoma using tree structured wavelet transforms , 2003, Comput. Methods Programs Biomed..

[20]  Norimichi Tsumura,et al.  Independent Component Analysis of Skin Color Image , 1998, CIC.

[21]  Josef Smolle,et al.  Tissue counter analysis of benign common nevi and malignant melanoma , 2003, Int. J. Medical Informatics.

[22]  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.

[23]  D. Leotta,et al.  Image processing techniques for quantitative analysis of skin structures. , 1999, Computer methods and programs in biomedicine.

[24]  M. Emre Celebi,et al.  Unsupervised border detection of skin lesion images , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[25]  Lucila Ohno-Machado,et al.  A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions , 2001, J. Biomed. Informatics.

[26]  R Marchesini,et al.  Image Analysis in the RGB and HS Colour Planes for a Computer-Assisted Diagnosis of Cutaneous Pigmented Lesions , 1998, Tumori.

[27]  Sang Uk Lee,et al.  On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques , 1990, Pattern Recognit..

[28]  Paul Geladi,et al.  Skin cancer identification using multifrequency electrical impedance-a potential screening tool , 2004, IEEE Transactions on Biomedical Engineering.

[29]  Mark J. Carlotto,et al.  Histogram Analysis Using a Scale-Space Approach , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Jun Zhang,et al.  A novel multiresolution color image segmentation technique and its application to dermatoscopic image segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[31]  Ralph Braun,et al.  The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. , 2005, Archives of dermatology.

[32]  Dmitry B. Goldgof,et al.  A vision-based technique for objective assessment of burn scars , 1998, IEEE Transactions on Medical Imaging.

[33]  Pietro Rubegni,et al.  Automated diagnosis of pigmented skin lesions , 2002, International journal of cancer.

[34]  R Marchesini,et al.  In vivo SPECTROPHOTOMETRIC EVALUATION OF NEOPLASTIC AND NON‐NEOPLASTIC SKIN PIGMENTED LESIONS–I. REFLECTANCE MEASUREMENTS , 1991, Photochemistry and photobiology.

[35]  Josef Smolle,et al.  EARLY DIAGNOSIS OF MALIGNANT MELANOMA BY SURFACE MICROSCOPY , 1987, The Lancet.

[36]  Gerard de Haan,et al.  Automatic imaging sysem with decision support for inspection of pigmented skin lesions and melanoma diagnosis. , 2009 .

[37]  A. Gutenev,et al.  Acquisition-time image quality control in digital dermatoscopy of skin lesions. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[38]  P. Schmid-Saugeon.  Lesion detection in dermatoscopic images using anisotropic diffusion and morphological flooding , 1999 .

[39]  M. Mihm,et al.  The performance of MelaFind: a prospective multicenter study. , 2011, Archives of dermatology.

[40]  Zhao Zhang,et al.  Neural networks skin tumor diagnostic system , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

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

[42]  H. Kittler,et al.  Diagnostic accuracy of dermoscopy. , 2002, The Lancet. Oncology.

[43]  K Wolff,et al.  Computer-aided epiluminescence microscopy of pigmented skin lesions: the value of clinical data for the classification process , 2000, Melanoma research.

[44]  Ilias Maglogiannis,et al.  A system for the acquisition of reproducible digital skin lesions images. , 2003, Technology and health care : official journal of the European Society for Engineering and Medicine.

[45]  Valery Tuchin,et al.  New closed-form approximation for skin chromophore mapping. , 2011, Journal of biomedical optics.

[46]  Atam P Dhawan,et al.  Optical Imaging Modalities for Biomedical Applications , 2010, IEEE Reviews in Biomedical Engineering.

[47]  Graham A Colditz,et al.  Risk factors and individual probabilities of melanoma for whites. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  Renato Marchesini,et al.  Automated melanoma detection with a novel multispectral imaging system: results of a prospective study , 2005, Physics in medicine and biology.

[49]  R. Pariser,et al.  Primary care physicians' errors in handling cutaneous disorders. A prospective survey. , 1987, Journal of the American Academy of Dermatology.

[50]  Halina Kwasnicka,et al.  Melanocytic lesion images segmentation enforcing by spatial relations based declarative knowledge , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[51]  P Altmeyer,et al.  Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer‐aided analysis of data from pigmented skin lesions using digital dermoscopy , 2003, The British journal of dermatology.

[52]  Massimo Ferri,et al.  Qualitative asymmetry measure for melanoma detection , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[53]  Howard I Maibach,et al.  Tissue viability imaging: mapping skin erythema , 2009, 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.

[54]  S.E. Umbaugh,et al.  Feature extraction in image analysis. A program for facilitating data reduction in medical image classification , 1997, IEEE Engineering in Medicine and Biology Magazine.

[55]  Philippe Schmid.  Lesion detection in dermatoscopic images using anisotropic diffusion and morphological flooding , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[56]  G. Cevenini,et al.  Differentiation between pigmented Spitz naevus and melanoma by digital dermoscopy and stepwise logistic discriminant analysis , 2001, Melanoma research.

[57]  Xiaojing Yuan,et al.  SVM-based Texture Classification and Application to Early Melanoma Detection , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[58]  Michael A. King,et al.  Counting moles automatically from back images , 2005, IEEE Transactions on Biomedical Engineering.

[59]  T. Pavlidis.  Algorithms for Graphics and Image Processing , 1981, Springer Berlin Heidelberg.

[60]  R. Joe Stanley,et al.  Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color , 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.

[61]  Guillermo Sapiro,et al.  Segmenting skin lesions with partial-differential-equations-based image processing algorithms , 2000 .

[62]  G. Surowka,et al.  Different Learning Paradigms for the Classification of Melanoid Skin Lesions Using Wavelets , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[63]  M Fimiani,et al.  Dysplastic naevus vs. in situ melanoma: digital dermoscopy analysis , 2005, The British journal of dermatology.