Authors:Tammineni Sreelatha,M.V. Subramanyam,M. N. Giri Prasad,
Texture in images can be utilized as a cue for different computer vision tasks as object identification and classification. This paper proposes CSTC-Mel Identification Model for texture classification, the feature representation which is low dimensional and training free, robust in nature for the texture description. The proposed technique is implemented in 3 phases such as ULL responses, feature computation, Feature encoding and the representation of image. Feature Computation is generated to categorize the texture structures and their connection by implementing linear and non-linear operators on the ULL responses of Gaussian Filter in the scale space, which is established based on steerable filters. Feature encoding through more than one level of thresholding or binary can be adopted to compute these feature computation into texture. Two encoding methods are designed which is robust in nature to the illumination changes and image rotation. The feature representation is explored to combine the discrete texture into the histogram representation. Our proposed model is tested on PH2 dataset. By comparing the experimental outcomes of proposed CSTC-Mel Identification Model with existing models, we can observe t at the proposed CSTC-Mel Identification Model identifies the skin cancer with accuracy of 93.81%.
Keywords:Texture Classification,Steerable Filter,Gaussian Filter,Feature Computation,Feature Encoding,
I. A. Madooei, M. S. Drew and H. Hajimirsadeghi, “Learning to Detect Blue–White Structures in Dermoscopy Images With Weak Supervision,” in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 779-786, March 2019.
II. A. Madooei, M. S. Drew, M. Sadeghi, and M. S. Atkins, “Automatic detection of blue-white veil by discrete colour matching in dermoscopy images,” in Medical Image Computing and Computer-Assisted Intervention MICCAI, ser. Lecture Notes in Computer Science, K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, Eds. Springer Berlin Heidelberg, 2013, no. 8151, pp. 453–460.
III. EbtihalAlmansour and M. ArfanJaffar, “Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features”, IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.4, April 2016.
IV. Fatima, R and Khan, Mohammed Zafar Ali and A, Govardhan and Dhruve, K P (2012) Computer Aided Multi-Parameter Extraction System to Aid Early Detection of Skin Cancer Melanoma. International Journal of Computer Science and Network Security, 12 (10). pp. 74-86. ISSN 1738-7906
V. G. A. S. Saroja and C. H. Sulochana, “Texture analysis of non-uniform images using GLCM,” 2013 IEEE Conference on Information & Communication Technologies, Thuckalay, Tamil Nadu, India, 2013, pp. 1319-1322.
VI. H. Ganster, A. Pinz, R. Rohrer, and E. W. ¨ et al., “Automated melanoma recognition,” Medical Imaging, IEEE Transactions on, vol. 20, no. 3, pp. 233–239, 2001.  I. Maglogiannis, S. Pavlopoulos, and D.
VII. H. Kittler, H. Pehamberger, K. Wolff, and M. Binder, “Diagnostic accuracy of dermoscopy,” The lancet oncology, vol. 3, pp. 159-165, 2002.
VIII. J. C. Kavitha and A. Suruliandi, “Texture and color feature extraction for classification of melanoma using SVM,” 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), Kovilpatti, 2016, pp. 1-6.
IX. Koutsouris, “An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images,” IEEE Transactions on Information Technology in Biomedicine, pp. 86–98, 2005.
X. Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., &Garpebring, A. (2019). Gray-level invariant Haralick texture features. PLOS ONE, 14(2), e0212110.
XI. M. E. Celebi, H. Iyatomi, W. V. Stoecker, R. H. Moss, H. S. Rabinovitz, G. Argenziano, and H. P. Soyer, “Automatic detection of blue-white veil and related structures in dermoscopy images,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 670–677, 2008.
XII. M. Moncrieff, S. Cotton, P. Hall, R. Schiffner, U. Lepski, and E. Claridge, “SIAscopy assists in the diagnosis of melanoma by utilizing computer vision techniques to visualise the internal structure of the skin,” Med Image Understanding Analysis, pp. 53-56, 2001.
XIII. M. Rademaker and A. Oakley, “Digital monitoring by whole body photography and sequential digital dermoscopy detects thinner melanomas,” J Prim Health Care, vol. 2, pp. 268-72, 2010.
XIV. O. Abuzaghleh, B. D. Barkana, and M. Faezipour, “Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention,” in Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island, 2014, pp. 1-6.
XV. O. Abuzaghleh, M. Faezipour and B. D. Barkana, “A comparison of feature sets for an automated skin lesion analysis system for melanoma early detection and prevention,” 2015 Long Island Systems, Applications and Technology, Farmingdale, NY, 2015, pp1-6.
XVI. S. Joseph and J. R. Panicker, “Skin lesion analysis system for melanoma detection with an effective hair segmentation method,” 2016 International Conference on Information Science (ICIS), Kochi, 2016, pp. 91-96.
XVII. S. K. Vengalil, N. Sinha and G. S. Raghavan, “Modified oriented Gaussian derivative filter based texture detection algorithm and parameter estimation,” 2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, 2015, pp. 1-6.
XVIII. S.Suer, S.Kockara, and M.Mete, “An improved border detection in dermoscopy images for density based clustering”, BMC bioinformatics.,vol.12, No.10,p.S12,2011.
XIX. Saha, Sujaya and Dr. Rajat Gupta. “An Automated Skin Lesion Diagnosis by using Image Processing Techniques.” (2014).
XX. Sreelatha, T., Subramanyam, M.V. & Prasad, M.N.G., “Early detection of skin cancer using Melanoma Segmentation Technique”, Journal of Medical Systems (May 2019) 43:190.
XXI. T. Mendonca, P. Ferreira, J. Marques, A. Marcal, and J. Rozeira, “PH2- a dermoscopic image database for research and benchmarking,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS). IEEE, 2013, pp. 5437–5440.
XXII. T. Wadhawan, N. Situ, K. Lancaster, X. Yuan, and G. Zouridakis, “SkinScan©: A portable library for melanoma detection on handheld devices,” in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, 2011, pp. 133-136l.
XXIII. T. Y. Satheesha, D. Satyanarayana, M. N. G. Prasad and K. D. Dhruve, “Melanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification,” in IEEE Journal of Translational Engineering in Health and Medicine, vol. 5, pp. 1-17, 2017, Art no. 4300117.
XXIV. TammineniSreelatha, M.V.Subramanyam, M.N.Giri Prasad. A Survey work on Early Detection methods of Melanoma Skin Cancer. Research J.Pharm. and Tech. 2019; 12(5): 2589-2596.
XXV. Torkashvand, Fatemeh and Mehdi Fartash. “Automatic Segmentation Of Skin Lesion Using Markov Random Field.” (2015).
XXVI. W. Freeman and E. Adelson, “The design and use of steerable filters,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 9, pp. 891–906, Sep. 1991.
XXVII. W.Stoecker, M.Wronkiwwiez, R.Chowdhury, R. J. Stanley, J. Xu, A. Bangert, B. Shrestha, D.A. Calcara, H.S. Rabinovitz, M. Oliviero, F. Ahmed, L.A. Perry and R. Drugge, “Detection of Granularity in dermoscopy images of malignant melanoma using color and texture features”, Compu.Med Imaging Graph, Vol.32.N0.8, pp 670-677, Dec.2008.View Download