Author(s): Maniraj S P, Sardar Maran P

Email(s): spmaniraj1986@gmail.com

DOI: 10.52711/0974-360X.2022.00807   

Address: Maniraj S P1, Sardar Maran P2
1Research Scholar, Sathyabama Institute of Science and Technology, Chennai - 600119, Tamil Nadu, India.
2Associate Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai - 600119, Tamil Nadu, India.
*Corresponding Author

Published In:   Volume - 15,      Issue - 10,     Year - 2022


ABSTRACT:
In this paper, clustering approaches are analyzed for skin lesion segmentation using dermoscopic images. Three widely used machine learning approaches for image segmentation are Centroid-based clustering (CBC). Fuzzy C-Means Clustering (FCMC), and Expectation-Maximization (EM)–E&M step algorithm. The difference between CBC and FCMC lies in the partitioning method. The former one uses hard partitioning, and the later uses a variable degree of membership. In the EM algorithm, statistical methods are employed for distance calculation whereas, in CBC, the Euclidean distance measure is used. The segmentation results of individual clustering approaches are combined to get the refined skin lesion. Results show that the combined segmentation provides promising results for skin lesion segmentation in comparison with CBC, FCMC and EM- M step algorithm.


Cite this article:
Maniraj S P, Sardar Maran P. Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images. Research Journal of Pharmacy and Technology 2022; 15(10):4807-1. doi: 10.52711/0974-360X.2022.00807

Cite(Electronic):
Maniraj S P, Sardar Maran P. Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images. Research Journal of Pharmacy and Technology 2022; 15(10):4807-1. doi: 10.52711/0974-360X.2022.00807   Available on: https://rjptonline.org/AbstractView.aspx?PID=2022-15-10-80


REFERENCES:
1.    Abuzaghleh O, Barkana BD, Faezipour M. Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention. InSystems, Applications and Technology Conference (LISAT), 2014 IEEE LongIsland 2014 May 2 (pp. 1-6). DOI:10.1109/LISAT.2014.6845199
2.    Al-abayechia AA, Guoa X, Tana WH, Jalabc HA. Automatic skin lesion segmentation with optimal colour channel from dermoscopic images. Science Asia. 2014 Feb 1;40(1):1-7. doi:10.2306/scienceasia15131874.2014.40S.001
3.    Anuj Rai, Piyush Singh. Implementation of Face Annotation with refined label content. Int. J. Tech. 2016; Vol. 6(1): 14-16. DOI: 10.5958/2231-3915.2016.00004.3
4.    Archana Jethale, Neethu V Nath, Tanvi Arawkar, Anaja Bajpeyi, Mrs Deepti Nirwal , Monument Informatica: A Tour based Guide system using Real Time Monument Recognition ,Research Journal of Engineering and Technology, Vol. 09 (04): 373-379,October- December 2018. DOI:10.5958/2321-581X.2018.00050.8
5.    B. Padmapriya, M. S. Sangeetha, G. Ramya Priya Nandhini, T. T. Anusha Devi. Detection of Malarial Parasites using Image Processing Techniques from Blood Smear Slides. Research J. Pharm. and Tech 2018; Vol.11(10): 4401-4406. DOI: 10.5958/0974-360X.2018.00805.3
6.    Basil K Varghese, Geraldine Bessie Amali D, Uma Devi K S. Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset. Research J. Pharm. and Tech 2019; Vol.12(2):644-648. DOI:10.5958/0974-360X.2019.00114.8
7.    G. Mahesh Kumar, K. Arun Kumar, P. Rajashekar Reddy, J. Tarun Kumar. A Novel Approach of Tumor Detection in Brain using MRI Scan Images. Research J. Pharm. and Tech. 2020; Vol.13(12):5914-5918. DOI: 10.5958/0974-360X.2020.01032.X
8.    Garnavi R, Aldeen M, Celebi ME, Bhuiyan A, Dolianitis C, Varigos G. Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels. International Journal of Medicine and Medical Sciences. 2010;1(2):126-34. Doi:10.1.1.310.1164
9.    Ganeshbabu TR. Computer aided diagnosis of glaucoma detection using digital fundus image. International journal of advances in signal and image sciences. 2015 Dec 20;1(1):1-1. DOI: 10.5507/bp.2015.053
10.    Gayathri K, Vaidhehi V. An Automatic Identification of Lung Cancer from different types of Medical Images. Research J. Pharm. and Tech. 2019; Vol.12(5):2109-2115. DOI: 10.5958/0974-360X.2019.00350.0
11.    K. Narasimhan, K. Vijayarekha. Automated Diagnosis of Age Related Macular degeneration from fundus image. Research J. Pharm. and Tech.Vol. 8(9): Sept, 2015; Page 1284-1288.DOI: 10.5958/0974 360X.2015.00233.4
12.    Lawand K. Segmentation of Dermoscopic  Images. IOSR Journal of Engineering. 2014 Apr;4(4):16-20.   doi.org/ 10.1.1.1064.316
13.    Meenakshi K, Ms. Safa M, Mr. Karthick T, Ms.Sivaranjani N, A Novel Study of Machine LearningAlgorithms for Classifying Health Care Data, Research Journal of Pharmacy and Technology,2017,Vol. 10(5):1429. DOI: 10.5958/0974-360X.2017.00253.0
14.    Mendonca T, Marcal AR, Vieira A, Nascimento JC, Silveira M, Marques JS, Rozeira J. Comparison of segmentation methods for automatic diagnosis of dermoscopy images. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 2007 Aug 22 (pp. 6572-6575).doi.org/10.1109/IEMBS.2007.4353865.
15.    Nasir M, Attique Khan M, Sharif M, Lali IU, Saba T, Iqbal T. An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microscopy research and technique. 2018 Feb 21. doi.org/10.1002/jemt.23009
16.    Pennisi A, Bloisi DD, Nardi D, Giampetruzzi AR, Mondino C, Facchiano A. Skin lesion image segmentation using Delaunay Triangulation for melanoma detection. Computerized Medical Imaging and Graphics. 2016 Sep 1;52:89-103. Doi:10.1016/j.compmedimag.2016.05.002
17.    R. M. Balajee, K. Venkatesh. A Survey on Machine Learning Algorithms and finding the best out there for the considered seven Medical Data Sets Scenario. Research J. Pharm. and Tech. 2019; Vol. 12(6):3059-3062. DOI: 10.52711/2349-2988.2021.00033
18.    Rajeshri Lanjewar, Tripti Sharma. A   Determining Approach for Customer Behavior Analysis Using K-Mean Clustering. Int. J. Tech. Vol.1(2): 125-129, July-Dec. 2011.
19.    Revathi V, Chithra A. A review on segmentation techniques in skin lesion images. Intl Research Journal of Engg and Tech (IRJET). 2015 Dec;2(09).
20.    Ruela M, Barata C, Mendonca T, Marques JS. On the role of shape in the detection of melanomas. In Image and Signal Processing and Analysis (ISPA), 2013 8th International Symposium on 2013 Sep 4 (pp. 268-273).
21.    Santanu Halder, Abul Hasnat, Debotosh Bhattacharjee,   Mita Nasipuri A. Novel Low Space Image Storing and Reconstruction Method by K-Means Clustering Algorithm,  International Journal of Technology, Vol . 4( 1), 186-196    , June. 2014.
22.    Selvaraj  A.  Segmentation  and   Classification of Skin Lesions Based on Texture Features. International Journal of Engineering Research and Applications. 2014 Jan 1;4(12):197-203.
23.    Shrikant Burje, Sourabh Rungta, Anupam Shukla. Detection and Classification of MRI Brain Images For Head/Brain Injury Using Soft Computing Techniques. Research J. Pharm. and Tech. 2017; Vol. 10(3): 715-720. DOI: 10.5958/0974-360X.2017.00134.2
24.    Shyamala Devi M, Sruthi A. N, Saranya Jothi C. MRI Liver Tumor Classification Using Machine Learning Approach and Structure Analysis. Research J. Pharm. and Tech 2018; Vol. 11(2):434-438. DOI: 10.5958/0974-360X.2018.00080.X
25.    Sindhu Priya. S, B. Ramamurthy. Lung Cancer Detection using Image Processing Technique. Research J. Pharm. and Tech 2018; Vol. 11(5):2045-2049. DOI: 10.5958/0974-360X.2018.00379.
26.    Situ N, Yuan X, Zouridakis G, Mullani N. Automatic segmentation of skin lesion images using evolutionary strategy. InImage Processing, 2007. ICIP 2007. IEEE International Conference on 2007 Sep 16 (Vol. 6,pp. VI-277). DOI:10.1016/j.bspc.2008.02.003
27.    Sood H, Shukla M. Segmentation of skin lesions from digital images using an optimized approach: Genetic algorithm. International Journal of Computer Science and Information Technologies. 2014 Oct;5(5):6831-7. doi.org/10.1016/j.patcog.2012.08.012
28.    Swathi K, Raghavendra CK. Melanoma Detection and Classification System using Artificial Neural Networks, International Journal of Trend in Research and Development. 2017, 4(3): 384-388. https://doi.org/10.29284/ijasis.6.1.2020.12-20
29.    Wang H, Moss RH, Chen X, Stanley RJ, Stoecker WV, Celebi ME, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies  SW. Modified watershed technique and post- processing for segmentation of skin lesions in dermoscopy images. Computerized Medical Imaging and Graphics. 2011 Mar 1;35(2):116-20.doi.org/ 10.1016/j.compmedimag.2010.09.006.

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