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Melanoma Diagnostic System using Non-Shannon Havrda Measure and Harris Corner Detector
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 181 - Number 17 |
| Year of Publication: 2018 |
| Authors: Neena Agrawal, Vineet Khanna |
10.5120/ijca2018917729
|
Neena Agrawal, Vineet Khanna . Melanoma Diagnostic System using Non-Shannon Havrda Measure and Harris Corner Detector. International Journal of Computer Applications. 181, 17 ( Sep 2018), 7-13. DOI=10.5120/ijca2018917729
Abstract
Malignant Melanoma is the deadliest type of skin cancer, is one of the most rapidly increasing cancers in the world. Malignant Melanoma is very difficult to treat and it must be diagnosed and excised during its earliest stages. In this work, we use a data set of 184 clinical dermatoscopic images of skin lesions, in which 144 images are of malignant lesion and 40 images are of benign lesion. we classify skin lesions as malignants from color photographic slides of the lesions. So we use color images of skin lesions, image processing techniques and artificial neural network to distinguish from benign pigmented lesions. In image processing we use Image level median filtering and entropy based segmentation technique. At the first step, we Consider clinical images of skin cancer patients using high speed cameras. After that we uses Medial filtering and histogram preprocessing to avoid uneven illuminance problem. Then Harris Corner detection method is implemented to characterize edges of the image for further analysis and implementation. Second, Otsu and Entropy based image segmentation algorithm is performed which improves the quality of the image. Its used for lesion extraction. In this work we use Machine learning based approach to optimize the classification error. In this we have instigated the performance of entropy based method against standard Otsu method and proved that the information theory based non-shannon entropy function of Havrda give far optimum performance against histogram based approach.
References
- W. Barhoumi, E. Zagrouba, “A Prelimary Approach for The Automated Recognition of malignant Melanoma”,Image Anal Stereol, pp.121-135, 2004.
- EzzeddineZagrouba and WalidBarhoumi, “A prelimary approach for the automated reconition of malignant melanoma”, Image Anal Stereol, 23:121-13, 2004
- David Houcque, “Introduction to MATLAB for Engineering Students”, Northwestern University, version 1.2, August 2005
- G. grammatikopoulos, A. Hatzigaidas, A. Papastergiou, P. Lazaridis, Z. Zaharis, D. Kamptaki, G. Tryfon, “Automated Malignant Melanoma Detection Using MATLAB”, International Conference on Data Networks, Communications & Computer Proceedings of the 5th WSEAS, Bucharest, Romania, October 16-17, 2006
- Dave Tahmoush and HananSamet, “Using Image Similarity and Asymmetry to Detect Breast Cancer”, Medical Imaging, Proc. of SPIE Vol. 6144, 61441S, (2006)
- F. Mai, Y. Hung, H. Zhong, and W. Sze,“A hierarchical approach for fast and robust ellipse extraction”,Pattern Recognition, 41(8):2512–2524, August 2008
- Andreas Blum, Iris Zalaudek, “Digital Image Analysis for Diagnosis of Skin Tumors”, Seminar in Cutaneous Medicine and Surgery, Elsevier Inc,27:11-15 © 2008
- Shekhar Singh, Dr P. R. Gupta, “Breast Cancer detection and classification using Neural Network”, International Journal of Advanced Engineering Sciences and Technologies, vol no. 6, issue no. 1, 004, 2009
- Michael D. Stubblefield, Michael, “Cancer rehabilitation principles and practice”, Demos ”, International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December, 2009, 1793-8201.
- Abbas, Q.; Celebi, M.; García, I. F. Skin tumor area extraction using an improved dynamic programming approach. Skin Research and Technology, 2012, v. 18(2): p. 133-142.
- Alcón, J. F.; Ciuhu, C.; Kate, W. T.; Heinrich, A.; Uzunbajakava, N.; Krekels, G.; Siem, D.; Haan, G. d. Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE Journal of Selected Topics in Signal Processing, 2009, v. 3(1): p. 14-25.
- Argenziano, G.; Fabbrocini, G.; Carli, P.; Giorgi, V. D.; Sammarco, E.; Delfino, M. 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. Archives of Dermatology, 1998, v. 134(12): p. 1563- 1570.
- Castiello, C.; Castellano, G.; Fanelli, A. M. Neuro-fuzzy analysis of dermatological images, in IEEE International Joint Conference on Neural Networks. 2004.
- Skin Cancer Facts With Statistics”, National council on skin cancer prevention ,Friday May 24, 2013.
- Barhoumi & E. Zagrouba, “A Prelimary Approach For The Automated Recognition Of Malignant Melanoma,” Image AnalStereol, pp. 121-135, 2004.
- W. Barhoumi & E. Zagrouba in: E.Damiani, R.J. Howlett, L.C. Jain, N.Ichalkaranje (Eds.), Boundaries Detection Based on Polygonal Approximation by Genetic Algorithms, Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies (KES 2002), Frontiers in artificial intelligence and applications, IOS Press 82(2), Amsterdam, pp. 621-7, 2002.
- Maciej Ogorzalek, Leszek Nowak, Grzegorz Surówka and Ana Alekseenko,”Modern Techniques for computer-aided melanoma diagnosis”,ISBN:978-953-307-571-6, Intech 2011
- G. Grammatikopoulos, A. Hatzigaidas, Papastergiou, P. Lazaridis, Z. Zaharis, D. Kampitaki, G. Tryfon, “Automated Malignant Melanoma Detection Using MATLAB”, Proceedings of the 5th WSEAS Int. Conf. on DATA NETWORKS, COMMUNICATIONS & COMPUTERS, Bucharest, Romania, October 16-17, 2006.
- Gautam,D.; Ahmed, M., “Melanoma Detection and Classification Using SVM Based Decision Support System “, in IEEE INDICON 2015, 17-20 December 2015.
- Singh, D.; Gautam, D.; Ahmed, M., “Detection techniques for melanoma diagnosis: A performance evaluation,” in Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on , vol., no., pp.567-572, 12-13 July 2014.
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