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Multidimensional Texture Characterization: On Analysis for Brain Tumor Tissues Using MRS and MRI

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Abstract

This paper investigates the efficacy of automated pattern recognition methods on magnetic resonance data with the objective of assisting radiologists in the clinical diagnosis of brain tissue tumors. In this paper, the sciences of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) are combined to improve the accuracy of the classifier, based on the multidimensional co-occurrence matrices to assess the detection of pathological tissues (tumor and edema), normal tissues (white matter — WM and gray matter — GM), and fluid (cerebrospinal fluid — CSF). The results show the ability of the classifier with iterative training to automatically and simultaneously recover tissue-specific spectral and structural patterns and achieve segmentation of tumor and edema and grading of high and low glioma tumor. Here, extreme learning machine – improved particle swarm optimization (ELM-IPSO) neural network classifier is trained with the feature descriptions in brain magnetic resonance (MR) spectra. This has the characteristics of varying the normal spectral pattern associated with tumor patterns along with imaging features. Validation was performed considering 35 clinical studies. The volumetric features extracted from the vectors of this matrix articulate some important elementary structures, which along with spectroscopic metabolite ratios discriminate the tumor grades and tissue classes. The quantitative 3D analysis reveals significant improvement in terms of global accuracy rate for automatic classification in brain tissues and discriminating pathological tumor tissue from structural healthy brain tissue.

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References

  1. American Cancer Society: Cancer Facts & Figures 2012. American Cancer Society, Atlanta, 2012

    Google Scholar 

  2. Aruna Devi B, Deepa SN: Brain tumor tissue characterization in 3D magnetic resonance images using improved PSO for extreme learning machine. Progress in Electromagnetics Research B 49:31–54, 2013

    Article  Google Scholar 

  3. Alparone L, Argenti F, Benelli G: Fast calculation of co-occurrence matrix parameters for image segmentation. Electronics Letters 26(1):23–24, 1990

    Article  Google Scholar 

  4. Bendszus M, Warmuth-Metz M, Klein R: MR spectroscopy in gliomatosis cerebri. American Journal of Neuroradiology 21:375–380, 2000

    CAS  PubMed  Google Scholar 

  5. Croteau D, Scarpace L, Hearshen D, Gutierrez J, Fisher JL, Rock JP, Mikkelsen T: Correlation between MRS imaging and image guided biopsies: semi quantitative and qualitative histopathological analyses of patients with untreated glioma. Neurosurgery 49:823–829, 2001

    CAS  PubMed  Google Scholar 

  6. Chris C, Alex Zijdenbos P, Evans CA: A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis 7:513–527, 2003

    Article  Google Scholar 

  7. Devos A, Simonetti AW, van der Graaf M, Lukas L, Suykens JA, Vanhamme L, Buydens LM, Heerschap A, Van Huffel S: The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. J Magn Reson. 173:218–228, 2005

    Article  CAS  PubMed  Google Scholar 

  8. Fei H, Hai-Fen Y, Qing-Hua L: An improved Extreme learning machine based on particle swarm optimization. Proc. of Int. conf. on Intelligent Computing: 699–704, 2012.

  9. Fuster Garcia E, Tortajada S, Vicente J, Robles M, García Gómez JM: Extracting MRS discriminant functional features of brain tumors. NMR Biomed, 2012. doi:10.1002/nbm.2895

    Google Scholar 

  10. Garcia Gomez JM: Brain tumor classification using magnetic resonance spectroscopy. Tumors of the Central Nervous System 3:5–19, 2011

    Google Scholar 

  11. Georgiadis P, Kostopoulos S, Cavouras D, Glotsos D, Kalatzis I, Sifaki K, Malamas M, Solomou E, Nikiforidis G: Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means pattern recognition. Magnetic Resonance Imaging 29:525–535, 2011

    Article  PubMed  Google Scholar 

  12. Haralick RM, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3:610–621, 1973

    Article  Google Scholar 

  13. Huang GB, Zhu QY, Siew CK: Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501, 2006

    Article  Google Scholar 

  14. Howe FA: Opstad KS:1H MR spectroscopy of brain tumors and masses. NMR Biomed 16:123–131, 2003

    Article  CAS  PubMed  Google Scholar 

  15. Kovalev VA, Kruggel F, Gertz HJ, von Cramon Y: Structural brain asymmetry as revealed by 3D texture analysis of anatomical MR images. Proc. of Int. Conf. on Pattern Recognition, Quebec: 808–811, 2002.

  16. Kovalev VA, Petrou M, Suckling K: Detection of structural differences between the brains of schizophrenic patients and controls. Psy. Research: Neuro-imaging 124:177–189, 2003

    Google Scholar 

  17. Kovalev VA, Kruggel F, von Cramon DY, Gertz HJ: Three-dimensional texture analysis of MRI brain datasets. IEEE Trans. on Medical Imaging 20(5):424–433, 2001

    Article  CAS  Google Scholar 

  18. Luts J, Laudadio T, Idema AJ, Simonetti AW, Heerschap A, Vandermeulen D, Suykens JAK, Van Huffel S: Nosologic imaging of the brain: segmentation and classification using MRI and MRSI. NMR in Biomedicine 22(4):374–390, 2009a

    Article  Google Scholar 

  19. Luts J, Heerschap A, Suykens JAK, Van Huffel S: A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. Artificial Intelligence in Medicine 40(2):87–102, 2007

    Article  PubMed  Google Scholar 

  20. Luts J, Martinez-Bisbal MC, Van Cauter S. Molla, Piquer E, Suykens JA, Himmelreich K, Celda, UB, Van Huffel S: Differentiation between brain metastases and glioblastoma multiforme based on MRI, MRS and MRSI. Proc. of the IEEE International Symposium on Computer-Based Medical Systems (CBMS), New Mexico: 1–8, 2009b.

  21. Majós C, Aguilera C, Cos M, Camins A, Candiota AP, Delgado-Goñi T, Samitier A, Castañer S, Sánchez JJ, Mato D, Acebes JJ, Arús C: In vivo proton magnetic resonance spectroscopy of intraventricular tumours of the brain. Eur Radiol 19(8):2049–2059, 2009

    Article  PubMed  Google Scholar 

  22. Mahmoud GD, Toussaint G, Constans JM, de Certaines JD: Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn. Reson Imaging 21:983–987, 2003

    Article  Google Scholar 

  23. Nelson SJ: Analysis of volume MRI and MR spectroscopic imaging data for the evaluation of patients with brain tumors. Magnetic Resonance in Medicine 46:228–239, 2001

    Article  CAS  PubMed  Google Scholar 

  24. Simonetti AW, Melssen WJ, de Szabo Edelenyi F, van Asten JJ, Heerschap A, Buydens LM: Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR in biomedicine 18:34–43, 2005

    Article  PubMed  Google Scholar 

  25. Soffietti R, Baumert BG, Bello L, von Deimling A, Duffau H, Fre’nay M, Grisold W, Grant R, Graus F, Hoang-Xuan K: Guidelines on management of low-grade gliomas: report of an EFNS–EANO* Task Force. European Journal of Neurology 17:1124–1133, 2010

    Article  CAS  PubMed  Google Scholar 

  26. Simon KW, Kaus M, Jolesz FA, Kikinis R: Adaptive, template moderated, spatially varying statistical classification. Medical Image Analysis 4(1):43–55, 2000

    Article  Google Scholar 

  27. Weibei Dou, Aoyan Dong, Shaowu Li, Ping Chi, Jean-Marc Constans: Glioma Tissue Modelling by combing the information of MRI and in vivo Multivoxel MRS. Proceedings of Int. Conf. on Bioinformatics and, Biomedical Engineering (iCBBE2010), China:1–4, 2010.

  28. Wang Q, Eirini Karamani L, Erickson M, Uday Kanamalla S, Vasileios M: Classification of brain tumors using MRI and MRS data. Proc. of SPIE 6514:65140S1–65140S8, 2007

    Article  Google Scholar 

  29. Fan Y, Shen D: Integrated feature extraction and selection for neuroimage classification. Proceedings of SPIE 7259:72591U, 2009

    Article  Google Scholar 

  30. Zhu QY, Qin AK, Suganthan PN, Huang GB: Evolutionary extreme learning machine. Pattern Recognition. 38(10):1759–1763, 2005

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Council of Scientific and Industrial Research (CSIR), New Delhi, India, with reference 09/1073/(0001)/2012. The authors thank PSG IMSR & Hospitals, Coimbatore, Tamilnadu, India, for providing clinical data after the approval of the ethics committee on clinical information.

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Authors and Affiliations

  1. Department of EEE, Anna University — Regional Centre Coimbatore, Jothipuram, Coimbatore, 641047, India

    Deepa Subramaniam Nachimuthu & Arunadevi Baladhandapani

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  1. Deepa Subramaniam Nachimuthu
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  2. Arunadevi Baladhandapani
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Correspondence to Arunadevi Baladhandapani.

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Nachimuthu, D.S., Baladhandapani, A. Multidimensional Texture Characterization: On Analysis for Brain Tumor Tissues Using MRS and MRI. J Digit Imaging 27, 496–506 (2014). https://doi.org/10.1007/s10278-013-9669-5

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  • DOI: https://doi.org/10.1007/s10278-013-9669-5

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