Brain MRI Image Analysis and Segmentation using Machine Learning

Authors

  • Swaroopa H N  Department of PG studies and Research in Electronics, Kuvempu University, Shankaraghatta, India
  • Basavaraj N Jagadale  Department of PG studies and Research in Electronics, Kuvempu University, Shankaraghatta, India
  • Ajaykumar Gupta  Department of PG studies and Research in Electronics, Kuvempu University, Shankaraghatta, India

DOI:

https://doi.org/10.32628/IJSRSET12293142

Keywords:

Magnetic resonance image (MRI) data, Discrete wavelet transform (DWT), Median filtering, Original fuzzy c-means clustering, Support Vector Machine (SVM) classifier.

Abstract

The brain magnetic resonance imaging (MRI), analysis and segmentation plays one of the crucial roles in medical diagnosis and facilitates in an early detection of diseases in critical medical conditions, Due to the structural complexity and type of the tumor, radiologists are facing difficulties in extracting essential features of the image which are crucial in treating the patient. Therefore, correct, and meaningful segmentation of brain MRI is a challenging task and is required for further processing. This article proposes machine learning based automatic brain MRI segmentation and classification. The pre-processing step is the vital part of the algorithm, where the discrete wavelet transforms (DWT) and median filtering help in identifying and pointing the exact location of the tumor. The preprocessed image is further segmented by an improved original Fuzzy C-means (FCM) clustering technique. The feature extraction and classification is performed by support vector machine (SVM) classifier. It is found that the simulation associated with ground truth data provides better segmentation results in terms of accuracy, sensitivity, and dice coefficient.

References

  1. Bashayer Fouad Marghalani, Muhammad Arif, “Automatic Classification of Brain Tumor and Alzheimer's Disease in MRI”, 16th International Learning & Technology Conference 2019, Procedia Computer Science 163 (2019) 78–84.
  2. Chinnu A, “MRI Brain Tumor Classification Using SVM and Histogram Based Image Segmentation”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2), 2015, 1505-1508.
  3. Mr. T. Sathies Kumar, K. Rashmi, Sreevidhya Ramadoss, L.K. Sandhya, T.J. Sangeetha, “Brain Tumor Detection Using SVM Classifier”, 2017 IEEE 3rd International Conference on Sensing, Signal Processing and Security (ICSSS), May 2017, IEEE, 978-1-5090-4929-5 DOI: 10.1109/SSPS.2017.8071613.
  4. Kimia Rezaei and Hamed Agahi, “Malignant and benign brain tumor segmentation and classification using SVM with weighted kernel width”, Signal & Image Processing: An International Journal (SIPIJ), Vol.8, No.2, April 2017.
  5. Ms. Shraddha Vyas, Mr. Hardik S. Jayswal, Dr. Amit P Ganatra, “Brain Tumor Detection and Classification using Image Processing and Machine Learning”, International Journal of Future Generation Communication and Networking, Vol. 13, No. 3, (2020), pp. 1445–1450.
  6. Rafiqul Islam, Shah Imran, Md. Ashikuzzaman, Md. Munim Ali Khan, Detection and Classification of Brain Tumor Based on Multilevel Segmentation with Convolutional Neural Network, J. Biomedical Science and Engineering, 2020, Vol. 13, (No. 4), pp: 45-53, doi.org/10.4236/jbise.2020.134004.
  7. Swaroopa H. N, Jagadale B. N, Priya B.S, “Bio-medical image Segmentation using Wavelet Based Fusion Technique”, Biomed Pharmacol Journal 2022;15(2).
  8. Manaswini Jena, SmitaPrava Mishra, Debahuti Mishra, “A survey on applications of machine learning techniques for medical image segmentation”, International Journal of Engineering & Technology, January 2018,7 (4) (2018) 4489-4495.DOI: 10.14419/ijet. v7i4.19005.
  9. Madina Hamiane, Fatema Saeed, “SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 7, No. 5, October 2017, pp. 2555~2564 ISSN: 2088-8708, DOI: 10.11591/ijece. v7i1.pp2555-2564.
  10. Heba Mohsen, El-Sayed Ahmed El-Dahshan, Abdel-Badeeh M. Salem, “A Machine Learning Technique for MRI Brain Images”, The 8th International Conference on Informatics and Systems (INFOS2012), January 2012– 14-16 May Bio-inspired Optimization Algorithms and Their Applications Track,
  11. Youguo Li, Haiyan Wu, “A Clustering Method Based on K-Means Algorithm”, 2012 International Conference on Solid State Devices and Materials Science, Elsevier Physics Procedia 25 (2012) 1104 – 1109.
  12. Sonika Dhankhar, Dr. T. V. Prasad, Shobha Tyagi, Brain MRI Segmentation using K- means Algorithm, March 2010, DOI: 10.13140/RG.2.1.4979.0567
  13. Jianwei Liu1, a, Lei Guo1, b, “An Improved K-means Algorithm for Brain MRI Image Segmentation”, 3rd International Conference on Mechatronics, Robotics and Automation (ICMRA 2015).
  14. Pragati Shrivastava, Piyush Singh, Gaurav Shrivastava, Pragati Shrivastava et al, “Image Classification using SOM and SVM Feature Extraction”, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5 (1), 2014, 264-271.
  15. Mohd Fauzi Bin Othman, Noramalina Bt Abdullah, Nurul Fazrena Bt Kamal, “MRI brain classification using support vector machine”, 978-1-4577-0005-7/11/$26.00 ©2011 IEEE.
  16. Noramalina Abdullah, Umi Kalthum Ngah, Shalihatun Azlin Aziz, “Image Classification of Brain MRI Using Support Vector Machine”, 978-1-61284-896-9/11/$26.00 ©2011 IEEE.
  17. Dzung L. Pham, Chenyang Xu, and Jerry L. Prince, “Current methods in medical image segmentation”, Annu. Rev. Biomed. Eng. 2000. 02:315–37.
  18. Abhishek Bal, Minakshi Banerjee, Amlan Chakrabarti, Punit Sharma, “MRI Brain Tumor Segmentation and Analysis using Rough-Fuzzy C-Means and Shape Based Properties”, Journal of King Saud University, Computer and Information Sciences, 1319-1578, https://doi.org/10.1016/j.jksuci.2018.11.001.
  19. Yogita K. Dubey and Milind M. Mushrif, “FCM Clustering Algorithms for Segmentation of Brain MR Images”, Hindawi Publishing Corporation Advances in Fuzzy Systems, Volume 2016, Article ID 3406406, 14 pages, http://dx.doi.org/ 10.1155/2016/3406406.
  20. Rodrigo Dalvit Carvalho da Silva, Thomas Richard JenkyN, Victor Alexander Carranza, “Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation”, Language Models, 2021 by the authors. Licensee MDPI, Basel, Switzerland,J. Pers.Med. 2021, 11, 310, https://doi.org/10.3390/jpm11040310.
  21. Nan Zhang, Su Ruan, Stéphane Lebonvallet, Qingming Liao, Yuemin Zhu, “Multi-kernel svm based classification for brain tumor segmentation of MRI multi-sequence”, 978-1-4244-5654-3/09/$26.00 ©2009 IEEE.
  22. Karlijn, J.van, Stralen vianda, S.Steljohannes, B.Reitsma kitty, J.Jager. “Diagnostic methods in: sensitivity, specificity, and other measures of accuracy”. https://doi.org/ 10.1038/ki.2009.92.
  23. Bertels, J. et.al and T.E. “Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice”, Medical Image Computing and Computer Assisted Invention – MICCAI 2019. Vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-12-30

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

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How to Cite

[1]
Swaroopa H N, Basavaraj N Jagadale, Ajaykumar Gupta "Brain MRI Image Analysis and Segmentation using Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 6, pp.202-212, November-December-2023. Available at doi : https://doi.org/10.32628/IJSRSET12293142