Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features

Melanoma is one of the deadliest forms of cancer; hence, great effort has been put into the development of diagnosis methods for this disease. This paper addresses two different systems for the detection of melanomas in dermoscopy images. The first system uses global methods to classify skin lesions, whereas the second system uses local features and the bag-of-features classifier. This paper aims at determining the best system for skin lesion classification. The other objective is to compare the role of color and texture features in lesion classification and determine which set of features is more discriminative. It is concluded that color features outperform texture features when used alone and that both methods achieve very good results, i.e., Sensitivity = 96% and Specificity = 80% for global methods against Sensitivity = 100% and Specificity = 75% for local methods. The classification results were obtained on a data set of 176 dermoscopy images from Hospital Pedro Hispano, Matosinhos.

[1]  Masafumi Hagiwara,et al.  An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm , 2008, Comput. Medical Imaging Graph..

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

[3]  Barbara Caputo,et al.  Recognition with local features: the kernel recipe , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Jorge S. Marques,et al.  Detecting the pigment network in dermoscopy images: A directional approach , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Fahad Shahbaz Khan,et al.  Top-down color attention for object recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  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.

[9]  R. H. Moss,et al.  A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images , 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.

[10]  Arivazhagan Selvaraj,et al.  Texture classification using Gabor wavelets based rotation invariant features , 2006, Pattern Recognit. Lett..

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

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

[13]  Fabrizio Argenti,et al.  Comments on "A New Algorithm for Border Description of Polarized Light Surface Microscopic Images of Pigmented Skin Lesions" , 2006, IEEE Trans. Medical Imaging.

[14]  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.

[15]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[16]  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.

[17]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[19]  Peter Shirley,et al.  oRGB: A Practical Opponent Color Space for Computer Graphics , 2009, IEEE Computer Graphics and Applications.

[20]  Marek Elbaum,et al.  Computer-aided melanoma diagnosis. , 2002, Dermatologic clinics.

[21]  J. Tasic,et al.  Colour spaces: perceptual, historical and applicational background , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[22]  G. Qiu.  Indexing chromatic and achromatic patterns for content-based colour image retrieval , 2002, Pattern Recognit..

[23]  G. Argenziano,et al.  Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. , 1998, Archives of dermatology.

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

[25]  K Wolff,et al.  Epiluminescence microscopy of small pigmented skin lesions: short-term formal training improves the diagnostic performance of dermatologists. , 1997, Journal of the American Academy of Dermatology.

[26]  Gerald Schaefer,et al.  Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods , 2013, 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.

[27]  Mai S. Mabrouk,et al.  Automatic Detection of Melanoma Skin Cancer using Texture Analysis , 2012 .

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

[29]  M. Stella Atkins,et al.  A novel method for detection of pigment network in dermoscopic images using graphs , 2011, Comput. Medical Imaging Graph..

[30]  R. H. Moss,et al.  Colour histogram analysis for melanoma discrimination in clinical images , 2003, 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.

[31]  Randy H. Moss,et al.  Automatic detection of blue-white veil and related structures in dermoscopy images , 2008, Comput. Medical Imaging Graph..

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

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

[34]  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 .

[35]  G. Zouridakis,et al.  Malignant melanoma detection by Bag-of-Features classification , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[37]  Robert Azencott,et al.  Texture Classification Using Windowed Fourier Filters , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[39]  Alfredo Paolillo,et al.  An improved procedure for the automatic detection of dermoscopic structures in digital ELM images of skin lesions , 2008, 2008 IEEE Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[40]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[41]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[42]  Jin Xu,et al.  Detection of granularity in dermoscopy images of malignant melanoma using color and texture features , 2011, Comput. Medical Imaging Graph..

[43]  Antonio Pietrosanto,et al.  Epiluminescence Image Processing for Melanocytic Skin Lesion Diagnosis Based on 7-Point Check-List: A Preliminary Discussion on Three Parameters , 2010 .

[44]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[46]  Gerald Schaefer,et al.  Objective evaluation of methods for border detection in dermoscopy images , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[47]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  C. Grana,et al.  Pigment distribution in melanocytic lesion images: a digital parameter to be employed for computer‐aided diagnosis , 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.

[49]  Alfredo Paolillo,et al.  Automatic Diagnosis of Melanoma: A Software System Based on the 7-Point Check-List , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[50]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.