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Analyzing and classifying MRI images using robust mathematical modeling

  • 1218: Engineering Tools and Applications in Medical Imaging
  • Published:
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Abstract

Medical imaging is an exponentially growing field, which consists of a set of tools and techniques used to extract useful information from medical images. Magnetic Resonance Imaging (MRI) is one of the most popular techniques among image modalities. This paper develops a linear model for classifying MRI images into the tumor and non-tumor categories. The proposed algorithm supports automatic extraction of features from brain MRI images, and focuses on extracting grey matter and white matter, so that the unhealthy MRI images can be isolated from the healthy MRI images. This technique takes advantage of preprocessing strategies and various filters for viable extraction and for classifying the brain MRI images. The samples of MRI images are taken from the BRAINIX and Neuroimaging data sources. The results are validated by applying the mathematical equations on 46 patients categorizing into 24 subjects as healthy and the remaining 22 as unhealthy. The novelty lies in formulating a general equation for both groups, which can be further used as a tool in computer-assisted medical systems for classifying patients.

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References

  1. Agrawal D, Minocha S, Namasudra S, Kumar S (2021) IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, Timisoara, Romania, pp 199–204

  2. Ajai ASR, Gopalan S (2020) Analysis of active contours without edge-based segmentation technique for brain tumor classification using SVM and KNN classifiers. In: Jayakumari J, Karagiannidis GK, Ma M, Hossainpp SA (eds) Advances in Communication Systems and Networks. Springer, Berlin, pp 1–10

    Google Scholar 

  3. Alguliyev RM et al (2020) Efficient algorithm for big data clustering on single machine. CAAI Trans Intell Technol 5(1):9–14

    Article  Google Scholar 

  4. Ali HM et al (2021) Planning a secure and reliable IoT-enabled FOG-assisted computing infrastructure for healthcare. Cluster Comput. https://doi.org/10.1007/s10586-021-03389-y

    Article  Google Scholar 

  5. Ashraf R et al (2020) Deep convolution neural network for big data medical image classification. IEEE Access 8:105659–105670

    Article  Google Scholar 

  6. Bansal A, Bhatia M, Yadav D (2016) Survey and comparative study on statistical tools for medical images. Adv Sci Lett 21(1):74–77

    Google Scholar 

  7. Bhatia S (2020) A comparative study of opinion summarization techniques. IEEE Trans Social Comput Syst 1–8. https://doi.org/10.1109/TCSS.2020.3033810

  8. Bhatia M, Bansal A, Yadav D, Gupta P (2015) A proposed stratification approach for MRI images. Indian J Sci Technol 8(22):1–12

    Article  Google Scholar 

  9. Chakraborty R, Verma G, Namasudra S (2021) IFODPSO-based multi-level image segmentation scheme aided with Masi entropy. J Ambient Intell Humaniz Comput 12:7793–7811. https://doi.org/10.1007/s12652-020-02506-w

    Article  Google Scholar 

  10. Chithra PL, Dheepa G (2018) An analysis of segmenting and classifying tumor regions in MRI images using CNN. Int J Pure Appl Math 118(2):1–12. https://acadpubl.eu/hub/2018-118-24/1/77.pdf

  11. Chithra PL, Dheepa G (2020)Di-phase midway convolution and deconvolution network for brain tumor segmentation in MRI images. Int J Imaging Syst Technol 30(3):674–686

    Article  Google Scholar 

  12. Conturo TE et al (1999) Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci 96(18):10422–10427

    Article  Google Scholar 

  13. Dev K, Khowaja SA, Bist AS, Saini V, Bhatia S (2020) Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks. arXiv:2011.00618

  14. Dhanith PRJ, Surendiran B, Raja SP (2021) A word embedding based approach for focused web crawling using the recurrent neural network. Int J Interact Multimed Artif Intell 6(6):122–132

    Google Scholar 

  15. Fong SJ, Li G, Dey N, Crespo RG, Fong SJ, Viedma EH (2020) Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. Int J Interact Multimed Artif Intell 6(1):132–140

    Google Scholar 

  16. Gregg C et al (1992) Segmentation techniques for the classification of brain tissue using magnetic resonance imaging. Psychiatry Res: Neuroimaging 45(1):33–51

    Article  Google Scholar 

  17. Hashemi RH, Bradley WG, Lisanti CJ (2010) MRI: The basics. Lippincott Williams & Wilkins, Philadelphia

  18. He J et al. Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI. https://doi.org/10.1101/2020.09.19.304758

  19. Hua L, Gu Y, Gu X, Xue J, Ni T (2021) A novel brain MRI image segmentation method using an improved multi-view fuzzy c-means clustering algorithm. Front NeuroSci. https://doi.org/10.3389/fnins.2021.662674

    Article  Google Scholar 

  20. Jiang J, Schmajuk N, Egner T (2012) Explaining neural signals in human visual cortex with an associative learning model. Behav Neurosci 126(4):575–581

    Article  Google Scholar 

  21. Kasihmuddin MSBM, Mansor MAB, Alzaeemi SA, Sathasivam S (2021) Satisfiability logic analysis via radial basis function neural network with artificial bee colony algorithm. Int J Interact Multimed Artif Intell 6(6):164–173

    Google Scholar 

  22. Kennedy DN, Haselgrove C, Riehl J, Preuss N, Buccigrossi R (2016) The NITRC image repository. Neuroimage. https://doi.org/10.1016/j.neuroimage.2015.05.074

    Article  Google Scholar 

  23. Kumar PM et al (2021) Clouds proportionate medical data stream analytics for internet of things-based healthcare systems. IEEE J Biomed Health Inf. https://doi.org/10.1109/JBHI.2021.3106387

    Article  Google Scholar 

  24. Leemput KV, Maes F, Vandermeulen D, Suetens P (2003) A unifying framework for partial volume segmentation of brain MR images. IEEE Trans Med Imaging 22(1):105–119

    Article  Google Scholar 

  25. Li S, Wang G, Yang J (2019) Survey on cloud model based similarity measure of uncertain concepts. CAAI Trans Intell Technol 4(4):223–230

    Article  Google Scholar 

  26. Liu J et al (2014) A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci Technol 19(6):578–595

    Article  MathSciNet  Google Scholar 

  27. Mihaylova A, Georgieva V, Petrov P (2020) Multistage approach for automatic spleen segmentation in MRI sequences. Int J Reasoning-Based Intell Syst 12(2):128–137

    Google Scholar 

  28. Miller AKH, Alston RL, Corsellis JAN (1980) Variation with age in the volumes of grey and white matter in the cerebral hemispheres of man: measurements with an image analyser. Neuropathol Appl Neurobiol 6(2):119–132

    Article  Google Scholar 

  29. Namasudra S (2020) Fast and secure data accessing by using DNA computing for the cloud environment. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2020.3046471

    Article  Google Scholar 

  30. Namasudra S, Roy P, Vijayakumar P, Audithan S, Balamurugan B (2017) Time efficient secure DNA based access control model for cloud computing environment. Futur Gener Comput Syst 73:90–105

    Article  Google Scholar 

  31. Namasudra S, Deka GC, Bali R (2018) Applications and future trends of DNA computing. In: Namasudra S, Deka GC (eds) Advances of DNA Computing in Cryptography. Taylor & Francis, pp 181–192

  32. Namasudra S, Chakraborty R, Majumder A, Moparthi NR (2020) Securing multimedia by using DNA based encryption in the cloud computing environment. ACM Trans Multimed Comput Commun Appl 16(3s). https://doi.org/10.1145/3392665

  33. Hamzenejad A, Ghoushchi SJ, Baradaran V (2021) Clustering of brain tumor based on analysis of MRI images using Robust Principal Component Analysis (ROBPCA) algorithm. BioMed Res Int. https://doi.org/10.1155/2021/5516819

  34. Namasudra S, Dhamodharavadhani S, Rathipriya R (2021) Nonlinear neural network based forecasting model for predicting COVID-19 cases. Neural Process Lett. https://doi.org/10.1007/s11063-021-10495-w

    Article  Google Scholar 

  35. Nikam PB, Shinde VD (2013) "MRI brain image classification and detection using distance classifier method in image processing" Int J Eng Res Technol 2(6):1980–1985

    Google Scholar 

  36. Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2(1):315–337

    Article  Google Scholar 

  37. Prastawa M, Bullitt E, Gerig GA (2004) Brain tumor segmentation framework based on outlier detectio. J Med Image Anal 8(3):275–283

    Article  Google Scholar 

  38. Ratan R, Sharma S, Sharma SK (2009) Brain tumor detection based on multi-parameter MRI image analysis. ICGST-GVIP J 9(3):9–16

    Google Scholar 

  39. Raut HT et al (2021) Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM. Soft Comput. https://doi.org/10.1007/s00500-021-06075-8

    Article  Google Scholar 

  40. Schalk G, Mellinger J (2010) A practical guide to brain–computer interfacing with BCI2000: General-purpose software for brain-computer interface research, data acquisition, stimulus presentation, and brain monitoring. Springer Science & Business Media, Springer, Berlin

  41. Sharif MI, Li JP, Khan MA, Saleem MA (2020) "Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images”. Pattern Recognit Lett 129:181–189

    Article  Google Scholar 

  42. Sharma M, Miglani N (2020) Automated brain tumor segmentation in MRI images using deep learning: overview, challenges and future. In: Dash S, Acharya BR, Mittal M, Abraham A, Kelemen A (eds) Deep Learning Techniques for Biomedical and Health Informatics. Springer, Berlin, pp 347–383

    Chapter  Google Scholar 

  43. Singh AK, Singla R (2020) Different approaches of classification of brain tumor in MRI using gabor filters for feature extraction. In: Pant M, Sharma TK, Verma OP, Singla R, Sikander A (eds) Soft Computing: Theories and Applications. Springer, Berlin, pp 1175–1188

    Google Scholar 

  44. Warfield S et al (1995) Laboratory investigation: Automatic identification of Gray Matter Structures from MRI to improve the Segmentation of White Matter Lesions. Comput Aided Surg 1(6):326–338

    Article  Google Scholar 

  45. Winkler AM et al (2010) Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 53(3):1135–1146

    Article  Google Scholar 

  46. Yildirim M (2019) Adapting Laplacian based filtering in digital image processing to a retina-inspired analog image processing circuit. Analog Integr Circuits Signal Process 100(3):537–545

    Article  Google Scholar 

  47. Zhao X, Li R, Zuo X (2019) Advances on QoS-aware web service selection and composition with nature-inspired computing. CAAI Trans Intell Technol 4(3):159–174

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University, Noida, India

    Madhulika Bhatia & Madhurima Hooda

  2. Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Hasa, Saudi Arabia

    Surbhi Bhatia

  3. Department of Computer Science and Engineering, National Institute of Technology Patna, Bihar, India

    Suyel Namasudra

  4. Universidad Internacional de La Rioja, Logroño, Spain

    Suyel Namasudra

  5. Faculty of Information Technology, Monash University, Clayton, Australia

    David Taniar

Authors
  1. Madhulika Bhatia
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  2. Surbhi Bhatia
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  3. Madhurima Hooda
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  4. Suyel Namasudra
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  5. David Taniar
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Correspondence to Suyel Namasudra.

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Bhatia, M., Bhatia, S., Hooda, M. et al. Analyzing and classifying MRI images using robust mathematical modeling. Multimed Tools Appl 81, 37519–37540 (2022). https://doi.org/10.1007/s11042-022-13505-8

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  • DOI: https://doi.org/10.1007/s11042-022-13505-8

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