G. Mahesh Kumar, K. Arun Kumar, P. Rajashekar Reddy, J. Tarun Kumar
email@example.com , firstname.lastname@example.org , email@example.com , firstname.lastname@example.org
G. Mahesh Kumar1, K. Arun Kumar2, P. Rajashekar Reddy3, Dr. J. Tarun Kumar4
1Assistant Professor, Department of Electronics and Communication Engineering, School of Engineering,
S R University, Ananthasagar (V), Dist-Warangal Urban, Telangana- 506371, India.
2Assistant Professor, Department of Electronics and Communication Engineering, CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana 501510, India.
3Assistant Professor, Department of Electronics and Communication Engineering, CVR College of Engineering, Vastunagar, Mangalpalli (V), Ibrahimpatnam (M), Rangareddy (D), Telangana 501510, India.
4Professor and Head, Department of Electronics and Communication Engineering, School of Engineering,
SR University, Ananthasagar (V), Dist-Warangal Urban, Telangana- 506371, India.
Volume - 13,
Issue - 12,
Year - 2020
Tumor in brain is one of the main severe diseases in human beings. Tumor detection in brain is crucial for its treatment. The correct location of tumor and its spread region is detected. Tumor is detected by using the property that the tumor has the more intensity in its region. In this implementation, MRI scan image is considered as the input to the system. Here the tumor detection is implemented in two major steps i.e. image pre-processing, post-processing. Preprocessing uses image enhancement techniques like noise removal, high-pass filtering, median filtering and de-blurring. In post-processing, the operations like thresholding, segmentation using watershed technique and morphological operations are implemented. The implemented system is applied on different images at various angles and the accurate location of tumor is identified. The implemented system gives the fast and efficient results.
Cite this article:
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; 13(12):5914-5918. doi: 10.5958/0974-360X.2020.01032.X
1. Ishita Maiti and Dr. Monisha Chakraborthy, “A New Method for Brain Tumor Segmentation Based on Watershed and Edge Detection Algorithms in HSV Colour Model” IEEE, 2012 National Conference on Computing and Communication Systems, Durgapur, 2012, pp. 01-05. doi: 10.1109/NCCCS.2012. 6413020
2. H. S. Abdulbaqi, Mohd Zubir, Omar, Mustafa and Abood, "Detecting brain tumor in Magnetic Resonance Images using Hidden Markov Random Fields and Threshold techniques," 2014 IEEE Student Conference on Research and Development, Batu Feringhi, 2014, pp.01-05, doi: 10.1109/SCORED.2014.7072963
3. M. Usman Akram and Anam Usman, "Computer aided system for brain tumor detection and segmentation," International Conference on Computer Networks and Information Technology, Abottabad, 2011, pp. 299-302, doi: 10.1109/ ICCNIT.2011.6020885
4. Raj and R. Shreeja, "Automatic brain tumor tissue detection in T-1 weighted MRI," IEEE, 2017 International Conference on Innovations in Information, Embedded and Communication System, Coimbatore, 2017, pp. 01-04, doi: 10.1109/ ICIIECS.2017.8276094
5. S. K. Chandra and M. Kumar, "Effective algorithm for benign brain tumor detection using fractional calculus," IEEE, 2018 IEEE Region 10 Conference, Jeju, Korea(South), 2018, p.p. 2408-2413, doi: 10.1109/ TENCON.2018.8650163.
6. E. Ulku and A. Y. Camuru, "Computer aided brain tumor detection with histogram equalization and morphological image processing techniques," 2013 International Conference on Electronics, Computer and Computation, Ankara, 2013, pp. 048-051, doi: 10.1109/ICECCO. 2013.6718225
7. Manisha, B. Radhakrishnan and L. P. Suresh, "Tumor region extraction using edge detection method in brain MRI images," IEEE, 2017 International Conference on Circuit, Power and Computing Technologies, Kolam, 2017, pp. 01-05, doi: 10.1109/ICCPCT.2017.8074326
8. M. Hunnur, A. Raut and S. Kulkarni, "Implementation of image processing for detection of brain tumors," IEEE, 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2017, pp. 717-722, doi: 10.1109/ICCMC.2017. 8282559
9. Kollem S, Reddy KRL,Rao DS. Denoising and segmentation of MR images using fourth order non-linear adaptive PDE and new convergent clustering. International Journal of Imaging Systems and Technology. 2018;1–15. https://doi.org/10.1002/ima.22302.
10. Sreedhar Kollem, Katta Rama Linga Reddy, and Duggirala Srinivasa Rao, "A Review of Image Denoising and Segmentation Methods Based on Medical Images," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 288-295, 2019.
11. A.Harshavardhan, Dr. Suresh Babu, Dr. T. Venugopal, “An Improved Brain Tumor Segmentation and Classification Method using SVM with various Kernels” International Journal of Pharmaceutical Research(IJPR) .Vol- 46, 2019 , pp 489-495.
12. A.Harshavardhan,Dr. Suresh Babu, Dr. T. Venugopal, “An Improved Brain Tumor Segmentation Method from MRI Brain Images” IEEE Conference International Conference On Emerging Computation and Information Technologies (ICECIT-2017) 0n 15-16 Dec.2017. Added to IEEE Xplore : 06 September 2018. ISBN: 978-1-5386-1094-7. DOI: 10.1109/ICECIT.2017.8453435
13. Nirisha Sriram, Thenmozhi, Samrithi Yuvaraj. Effects of Mobile Phone Radiation on Brain: A questionnaire based study. Research J. Pharm. and Tech. 8(7): July, 2015; Page 867-870.
14. Bhawna Goyal, Sunil Agrawal, B.S. Sohi, Ayush Dogra. Noise Reduction in MR brain image via various transform domain schemes. Research J. Pharm. and Tech. 2016; 9(7):919-924.
15. Sam Mirfendereski, Arash Shabani, Ayoob Rostamzadeh, Daryoush Fatehi. Molecular Imaging Using by Diffusion-Weighted Imaging of Brain Tumor Through Signal Intensity: Progress in Molecular Cancer Imaging. Research J. Pharm. and Tech. 2017; 10(6): 1767-1771.
16. Swarnakala, Natarajah Srikumaran. Brain Tumor Segmentation by EM Algorithm. Research J. Pharm. and Tech. 2017; 10(9): 3022-3024.
17. T. Sudhakar, Bethanney Janney. J, Haritha. D, Juliet Sahaya. M, Parvathy. V. Automatic Detection and Classification of Brain Tumor using Image Processing Techniques. Research J. Pharm. and Tech 2017; 10(11): 3692-3696.
18. C. Saranya Jothi, V. Usha, S. Alex David, Hijaj Mohammed. Abnormality Classification of Brain Tumor in MRI Images using Multiclass SVM. Research J. Pharm. and Tech. 2018; 11(3): 851-856.
19. Imayanmosha Wahlang, Pallabi Sharma, Goutam Saha, Arnab K. Maji. Brain Tumor Classification Techniques using MRI: A Study. Research J. Pharm. and Tech 2018; 11(10): 4764-4770.
20. Gayathri K, Vaidhehi V. An Automatic Identification of Lung Cancer from different types of Medical Images. Research J. Pharm. and Tech. 2019; 12(5):2109-2115.
21. Ganesan P, B.S. Sathish, R. Murugesan. A Simple Approach to Automated Brain Tumor Segmentation and Classification. Research J. Pharm. and Tech. 2019; 12(7):3564-3568.
22. Shashikant Agrawal, Yogesh Bahendwar. A Comparative Analysis of Thresholding Techniques for Denoising of MRI Image Using Wavelets. Research J. Engineering and Tech. 3(1): Jan.-Mar. 2012 page 34-37.
23. Rajdeep Chowdhury, Indrani Banerjee. Grey Scale Image Enhancement Using Proposed Modified Histogram Equalization Algorithm. Research J. Engineering and Tech. 5(4): Oct.-Dec., 2014 page 217-222.
24. Jaya Shrivastava. G.S. Verma. Retinex Theory for Image Enhancement. Research J. Science and Tech. 2010; 2(6): 160-161.