Analysis of MRI Data of Brain for CAD System

  • Authors

    • Girish D Bonde
    • Dr Manish Jain
    2018-04-15
    https://doi.org/10.14419/ijet.v7i2.17.11560
  • MRI, PTPSA, fBM, CAD, SOM, NN.

  • Magnetic resonance imaging (MRI) technologies are currently one of the most effective tools in the diagnosis of a wide variety of socially significant pathologies including cancer, arteriosclerosis, episodes. Ischemic and neurodegenerative diseases [1, 2, 3, 4].This paper gives detailed idea of pre-processing, and segmentation(FCM, soft and hard) of MRI brain tumor images. This paper also insights the machine learning(SOM, NN and SVM) approach for automatic classification(PTPSA, fBM) of brain tissues. Different performance evaluation parameter and similarity metrics are discuss to define the efficiency of computer-aided diagnostic (CAD) system.

     

     

  • References

    1. [1] Baker, A.D., 2014. Abnormal magnetic-resonance scans of the lumbar spine in asymptomatic subjects. A prospective investigation. In Classic Papers in Orthopaedics (pp. 245-247). Springer, London.
      [2] Muller, B.G., Ftterer, J.J., Gupta, R.T., Katz, A., Kirkham, A., Kurhanewicz, J., Moul, J.W., Pinto, P.A., Rastinehad, A.R., Robertson, C. and Rosette, J., 2014. The role of magnetic resonance imaging (MRI) in focal therapy for prostate cancer: recommendations from a consensus panel. BJU international, 113(2), pp.218-227.
      [3] Fatahi, M. and Speck, O., 2015. Magnetic resonance imaging (MRI): A review of genetic damage investigations. Mutation Research/Reviews in Mutation Research, 764, pp.51-63.
      [4] Rosenkrantz, A.B., Verma, S., Choyke, P., Eberhardt, S.C., Eggener, S.E., Gaitonde, K., Haider, M.A., Margolis, D.J., Marks, L.S., Pinto, P. and Sonn, G.A., 2016. Prostate magnetic resonance imaging and magnetic resonance imaging targeted biopsy in patients with a prior negative biopsy: a consensus statement by AUA and SAR. The Journal of urology, 196(6), pp.1613-1618.
      [5] Burth, S., Kickingereder, P., Eidel, O., Tichy, D., Bonekamp, D., Weberling, L., Wick, A., L?w, S., Hertenstein, A., Nowosielski, M. and Schlemmer, H.P., 2016. Clinical parameters outweigh diffusion-and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma. Neuro-oncology, 18(12), pp.1673-1679.
      [6] Prager, A.J., Martinez, N., Beal, K., Omuro, A., Zhang, Z. and Young, R.J., 2015. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. American Journal of Neuroradiology, 36(5), pp.877-885.
      [7] Kurth, F., Gaser, C. and Luders, E., 2015. A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM). Nature protocols, 10(2), p.293.
      [8] Suetens, P., 2017. Fundamentals of medical imaging. Cambridge university press.
      [9] Bushong, S.C. and Clarke, G., 2014. Magnetic resonance imaging: physical and biological principles. Elsevier Health Sciences.
      [10] Fowler, K.J., Maxwell, J., Saad, N.E., Yano, M., Raptis, C., Menias, C. and Narra, V., 2014. Magnetic resonance imaging of iatrogeny: understanding imaging artifacts related to medical devices. Abdominal imaging, 39(2), pp.411-423.
      [11] DU, R., Chiu, W.H.K., Lee, E.Y.P., Pang, H.M.H., Lam, E.Y.M. and Vardhanabhuti, V., 2017. Inter-session reproducibility and consistency of radiomic features after preprocessing as methods for quality control in MRI quantitative radiomics. In 7th Joint Scientific Meeting of The Royal College of Radiologists & Hong Kong College of Radiologists and 25th Annual Scientific Meeting of Hong Kong College of Radiologists.
      [12] Silva, A., Pinto, E. and Sampaio, R., 2016. Rotational alignment in patient-specific instrumentation in TKA: MRI or CT?. Knee Surgery, Sports Traumatology, Arthroscopy, 24(11), pp.3648-3652.
      [13] Silva, A., Pinto, E. and Sampaio, R., 2016. Rotational alignment in patient-specific instrumentation in TKA: MRI or CT?. Knee Surgery, Sports Traumatology, Arthroscopy, 24(11), pp.3648-3652.
      [14] McKenna, B.S., Theilmann, R.J., Sutherland, A.N. and Eyler, L.T., 2015. Fusing functional MRI and diffusion tensor imaging measures of brain function and structure to predict working memory and processing speed performance among inter-episode bipolar patients. Journal of the International Neuropsychological Society, 21(5), pp.330-341.
      [15] Crema, M.D., Cortinas, L.G., Lima, G.B., Abdalla, R.J., Ingham, S.J.M. and Skaf, A.Y., 2018. Magnetic resonance imaging-based morphological and alignment assessment of the patellofemoral joint and its relationship to proximal patellar tendinopathy. Skeletal radiology, 47(3), pp.341-349.
      [16] C?rdenas-Pe?a, D., Collazos-Huertas, D. and Castellanos-Dominguez, G., 2016. Centered kernel alignment enhancing neural network pretraining for MRI-based dementia diagnosis. Computational and Mathematical Methods in Medicine, 2016.
      [17] Fran￧a, L.K.L., Bitencourt, A.G.V., de Toledo Os?rio, C.A.B., Graziano, L., Guatelli, C.S., Souza, J.A. and Marques, E.F., 2018. Tumor size assessment of invasive breast cancers: which pathological features affect MRI-pathology agreement?. Applied Cancer Research, 38(1), p.2.
      [18] Weller-Fahy, D.J., Borghetti, B.J. and Sodemann, A.A., 2015. A survey of distance and similarity measures used within network intrusion anomaly detection. IEEE Communications Surveys & Tutorials, 17(1), pp.70-91.
      [19] Larsen, A.B.L., S?nderby, S.K., Larochelle, H. and Winther, O., 2015. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300.
      [20] Luo, H., Wang, J., Li, M., Luo, J., Peng, X., Wu, F.X. and Pan, Y., 2016. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics, 32(17), pp.2664-2671.
      [21] Cao, L., Jin, L., Tao, H., Li, G., Zhuang, Z. and Zhang, Y., 2015. Multi-focus image fusion based on spatial frequency in discrete cosine transform domain. IEEE signal processing letters, 22(2), pp.220-224.
      [22] Chen, K.T., Izquierdo-Garcia, D., Poynton, C.B., Chonde, D.B. and Catana, C., 2017. On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners. European journal of nuclear medicine and molecular imaging, 44(3), pp.398-407.
      [23] Blaiotta, C., Freund, P., Cardoso, M.J. and Ashburner, J., 2018. Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. NeuroImage, 166, pp.117-134.
      [24] Manj?n, J.V., Coupï¿©, P. and Buades, A., 2015. MRI noise estimation and denoising using non-local PCA. Medical image analysis, 22(1), pp.35-47.
      [25] Menon, N. and Ramakrishnan, R., 2015, April. Brain Tumor Segmentation in MRI images using unsupervised Artificial Bee Colony algorithm and FCM clustering. In Communications and Signal Processing (ICCSP), 2015 International Conference on(pp. 0006-0009). IEEE.
      [26] Goyal, B., Agrawal, S., Sohi, B.S. and Dogra, A., 2016. Noise Reduction in MR brain image via various transform domain schemes. Research Journal of Pharmacy and Technology, 9(7), pp.919-924.
      [27] Tan, C.H., Paul Hobbs, B., Wei, W. and Kundra, V., 2015. Dynamic contrast-enhanced MRI for the detection of prostate cancer: meta-analysis. American Journal of Roentgenology, 204(4), pp.W439-W448.
      [28] Rosenkrantz, A.B., Geppert, C., Grimm, R., Block, T.K., Glielmi, C., Feng, L., Otazo, R., Ream, J.M., Romolo, M.M., Taneja, S.S. and Sodickson, D.K., 2015. Dynamic contrast?enhanced MRI of the prostate with high spatiotemporal resolution using compressed sensing, parallel imaging, and continuous golden?angle radial sampling: Preliminary experience. Journal of Magnetic Resonance Imaging, 41(5), pp.1365-1373.
      [29] Pitchammal, M., Nisha, S.S. and Sathik, M.M., 2016. Noise Reduction in MRI Neck Image Using Adaptive Fuzzy Filter in Contourlet Transform. International Journal of Engineering Science, 2478.
      [30] Kagoiya, K. and Mwangi, E., 2017. A hybrid and adaptive non-local means wavelet based MRI denoising method with bilateral filter enhancement. International Journal of Computer Applications, 166(10).
      [31] Joseph, J., Sivaraman, J., Periyasamy, R. and Simi, V.R., 2016. Noise based computation of decay control parameter in nonlocal means filter for MRI restoration. Journal of Medical Imaging and Health Informatics, 6(4), pp.1027-1037.
      [32] Ali, H., Elmogy, M., El-Daydamony, E. and Atwan, A., 2015. Multi-resolution MRI brain image segmentation based on morphological pyramid and fuzzy c-mean clustering. Arabian Journal for Science and Engineering, 40(11), pp.3173-3185.
      [33] Deshmukh, P. and Malge, P.S., 2016. Classification of Brain MRI using Wavelet Decomposition and SVM. Entropy, 1(2), p.5.
      [34] Kanade, P.B. and Gumaste, P.P., 2015. Brain tumor detection using MRI images. Brain, 3(2).
      [35] Teh, V., Sim, K.S. and Wong, E.K., 2016, January. Extreme-Level Eliminating Brightness Preserving Bi-Histogram Equalization Technique for Brain Ischemic Detection. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV)(p. 69). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
      [36] Gupta, S., Gupta, R. and Singla, C., 2017. Analysis of image enhancement techniques for astrocytoma MRI images. International Journal of Information Technology, 9(3), pp.311-319.
      [37] Despotovi?, I., Goossens, B. and Philips, W., 2015. MRI segmentation of the human brain: challenges, methods, and applications. Computational and mathematical methods in medicine, 2015.
      [38] Pereira, S., Pinto, A., Alves, V. and Silva, C.A., 2016. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging, 35(5), pp.1240-1251.
      [39] Menon, N. and Ramakrishnan, R., 2015, April. Brain Tumor Segmentation in MRI images using unsupervised Artificial Bee Colony algorithm and FCM clustering. In Communications and Signal Processing (ICCSP), 2015 International Conference on(pp. 0006-0009). IEEE.
      [40] Adhikari, S.K., Sing, J.K., Basu, D.K. and Nasipuri, M., 2015. Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images. Applied Soft Computing, 34, pp.758-769.
      [41] Salimi-Khorshidi, G., Douaud, G., Beckmann, C.F., Glasser, M.F., Griffanti, L. and Smith, S.M., 2014. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage, 90, pp.449-468.
      [42] Jiang, Y., Zhu, C., Peng, W., Degnan, A.J., Chen, L., Wang, X., Liu, Q., Wang, Y., Xiang, Z., Teng, Z. and Saloner, D., 2016. Ex-vivo imaging and plaque type classification of intracranial atherosclerotic plaque using high resolution MRI. Atherosclerosis, 249, pp.10-16.
      [43] Chavan, N.V., Jadhav, B.D. and Patil, P.M., 2015. Detection and classification of brain tumors. International Journal of Computer Applications, 112(8).
      [44] Antonio Di Ieva, M.D., Pierre-Jean Le Reste, M.D., Carsin-Nicol, B., Ferre, M.J.C. and Cusimano, M.D., 2016. Diagnostic Value of Fractal Analysis for the Differentiation of Brain Tumors Using 3-Tesla Magnetic Resonance Susceptibility-Weighted Imaging.
      [45] Saravanan, G. and Krishnamoorthy, K., 2014. An Enhanced Implementation PTPSA Algorithm for Fractal Feature Extraction and Abnormal Tissue Segmentation.
      [46] Chinnasamy, G. and Vanitha, S., 2015. Fractional brownian motion and fractal analysis of brain mri images: a review. IJAR, 1(3), pp.21-24.
      [47] Vaishnavee, K.B. and Amshakala, K., 2015, March. An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier. In Engineering and Technology (ICETECH), 2015 IEEE International Conference on (pp. 1-6). IEEE.
      [48] Song, S.E., Seo, B.K., Cho, K.R., Woo, O.H., Son, G.S., Kim, C., Cho, S.B. and Kwon, S.S., 2015. Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study. Cancer Imaging, 15(1), p.1
      [49] Md Zia Ur Rahman, B.Malakonda Reddy, "Efficient SAR Image Segmentation Techniques using Biasfield Estimation", Journal of Scientific and Industrial Research, vol. 76, pp. 335-338, 2017.
      [50] P.V.V. Kishore, A.S.C.S. Sastry, Md. Zia Ur Rahman, "Double Technique for Improving Ultrasound Medical Images", Journal of Medical Imaging and Health Informatics, vol.6, no.3, pp.667-675, 2016.
      [51] M. Lakshmi, Md Zia Ur Rahman, "Efficient Speckle Noise Reduction Techniques for Synthetic Aperture Radars in Remote Sensing Applications", International Review of Aerospace Engineering Vol.9, no.10, 2016, pp.114-122.
      [52] M. Lakshmi, Md Zia Ur Rahman, "Analysis of Synthetic Aperture Radar Images using Brute Force Thresholding and Gradient Guide Filters", Journal of Theoretical and Applied Information Technology,Vol.93, no.1, 2016, pp.152-163.
      [53] B. Mala Konda Reddy, Md. Zia Ur Rahman, "Novel Segmentation Technique for Target Tracking in Synthetic Aperture Radars", International Journal of Control Theory and Applications,Vol.10, no.35, 2017, pp.335-341.

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    D Bonde, G., & Manish Jain, D. (2018). Analysis of MRI Data of Brain for CAD System. International Journal of Engineering & Technology, 7(2.17), 63-69. https://doi.org/10.14419/ijet.v7i2.17.11560