Advertisement

Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique

  • T. Kalaiselvi
  • P. Kumarashankar
  • P. SriramakrishnanEmail author
Original Paper
  • 25 Downloads

Abstract

Manually finding and segmenting brain tumor is a tedious process in MR brain images due to the unpredictable appearance of tissues with a different pattern, contour, mass, and positions. The proposed work has three phases automatic tumor diagnosis system for tumorous slice detection, segmentation, and visualization from MRI human head volumes. The proposed method has an automatic classification followed by segmentation and is called as patch-based updated run length region growing technique (PR2G). In the first phase, classification is done through training and testing process using SVM classifier with 8 × 8 patches. Three optimal features are chosen using infinite feature selection (IFS) method. The purpose of the first phase is to automatically cluster the input MRI image into a normal or tumorous slice and localize the tumor. The second phase aims to segment the tumor in abnormal tumorous slices identified by the first phase using run length region growing technique. Finally, the third phase contains a post metric evaluation like 3D tumor volume construction and estimation from actual and segmented tumor volume using Carelieri’s estimator. Classification accuracy is measured using sensitivity, specificity, accuracy, and error rates also calculated using false alarm (FA) and missed alarm (MA). Segmentation accuracy is calculated using Dice similarity, positive predictive value (PPV), sensitivity, and accuracy. Datasets used for this experiment are collected from whole brain atlas (WBA) and BraTS repositories. Experimental results show that the PR2G achieves 97% of classification accuracy and 80% of Dice segmentation accuracy.

Keywords

Tumor detection Feature extraction Tumor patches Brain tumor BraTS dataset Region growing Run length 

Notes

Acknowledgements

The authors wish to thank Dr. R. Rajeswaran, Radiologist, Sri Ramachandra University Medical College, Chennai, for their help in qualitative validation. Further, we acknowledge the support of 3D Doctor licensed software purchased under DST project: SP/YO/011/2007 used for volume rendering process of this research work.

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interests and the paper has not been submitted elsewhere.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Rodger JA: Discovery of medical big data analytics: Improving the prediction of traumatic brain injury survival rates by data mining patient informatics processing software hybrid Hadoop hive. Informatics in Medicine Unlocked 1:17–26, 2015CrossRefGoogle Scholar
  2. 2.
    Kalaiselvi T, Sriramakrishnan P: Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine. International Journal of Imaging Systems and Technology 28(3):163–174, 2018CrossRefGoogle Scholar
  3. 3.
    Zhang D, Shen D, Alzheimer's Disease Neuroimaging Initiative: Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one 7(3):e33182, 2012CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Shree NV, Kumar TNR: Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain informatics 5(1):23–30, 2018CrossRefGoogle Scholar
  5. 5.
    Khalil M, Ayad H, Adib A: Performance evaluation of feature extraction techniques in MR-brain image classification system. Procedia Computer Science 127:218–225, 2018CrossRefGoogle Scholar
  6. 6.
    Thillaikkarasi R, Saravanan S: An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM. J Med Syst 43(84), 2019.  https://doi.org/10.1007/s10916-019-1223-7
  7. 7.
    Wang, G., Li, W., Ourselin, S., & Vercauteren, T. (2017). Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In International MICCAI Brainlesion Workshop (pp. 178–190). Springer, Cham.Google Scholar
  8. 8.
    Liu J, Li M, Wang J, Wu F, Liu T, Pan Y: A survey of MRI-based brain tumor segmentation methods. Tsinghua Science and Technology 19(6):578–595, 2014CrossRefGoogle Scholar
  9. 9.
    Arakeri MP, Reddy GRM: Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. Signal, Image and Video Processing 9(2):409–425, 2015CrossRefGoogle Scholar
  10. 10.
    Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF: Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing 2018(1):97, 2018CrossRefGoogle Scholar
  11. 11.
    Sivakumar P, Ganeshkumar P: CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis. International Journal of Imaging Systems and Technology 27(2):109–117, 2017CrossRefGoogle Scholar
  12. 12.
    Nayak DR, Dash R, Majhi B: Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188–197, 2016CrossRefGoogle Scholar
  13. 13.
    Qurat-Ul-Ain, G. L., Kazmi, S. B., Jaffar, M. A., & Mirza, A. M. (2010). Classification and segmentation of brain tumor using texture analysis. Recent advances in artificial intelligence, knowledge engineering and data bases, 147–155.Google Scholar
  14. 14.
    Chen, L., Wu, Y., DSouza, A. M., Abidin, A. Z., Wismüller, A., & Xu, C. (2018). MRI tumor segmentation with densely connected 3D CNN. In Medical Imaging 2018: Image Processing (Vol. 10574, p. 105741F) International Society for Optics and Photonics.Google Scholar
  15. 15.
    Chugh S, Anand SM: Pixel run length based adaptive region growing (PRL-ARG) technique for segmentation of tumor from MRI images. In: International conference on computer and electrical engineering 4th (ICCEE 2011). ASME Press, 2011Google Scholar
  16. 16.
    Somasundaram K, Kalaiselvi T: Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images. Computers in biology and medicine 40(10):811–822, 2010CrossRefPubMedGoogle Scholar
  17. 17.
    Roffo, G., Melzi, S., & Cristani, M. (2015). Infinite feature selection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 4202–4210).Google Scholar
  18. 18.
    Cortes C, Vapnik V: Support-vector networks. Machine learning 20(3):273–297, 1995Google Scholar
  19. 19.
    Adams R, Bischof L: Seeded region growing. IEEE Transactions on pattern analysis and machine intelligence 16(6):641–647, 1994CrossRefGoogle Scholar
  20. 20.
    Gonzalez, R. C. (1992). RE woods digital image processing. Addison–Wesely Publishing Company.Google Scholar
  21. 21.
    Rosen GD, Harry JD: Brain volume estimation from serial section measurements: a comparison of methodologies. Journal of neuroscience methods 35(2):115–124, 1990CrossRefPubMedGoogle Scholar
  22. 22.
    http://www.med.harvard.edu/aanlib/, Last accessed 14th Aug 2019.
  23. 23.
    https://www.smir.ch/BRATS/Start2013, Last accessed 14th Aug 2019.
  24. 24.
    Dice LR: Measures of the amount of ecologic association between species. Ecology 26(3):297–302, 1945CrossRefGoogle Scholar
  25. 25.
    Bauer, S., Tessier, J., Krieter, O., Nolte, L. P., & Reyes, M. (2013). Integrated spatio-temporal segmentation of longitudinal brain tumor imaging studies. In International MICCAI Workshop on Medical Computer Vision (pp. 74–83). Springer, Cham.Google Scholar
  26. 26.
    Buendia P, Taylor T, Ryan M, John N: A grouping artificial immune network for segmentation of tumor images. Multimodal Brain Tumor Segmentation 1, 2013Google Scholar
  27. 27.
    Cordier, N., Menze, B., Delingette, H., & Ayache, N. (2013). Patch-based segmentation of brain tissues. In MICCAI challenge on multimodal brain tumor segmentation(pp. 6–17). IEEE.Google Scholar
  28. 28.
    Demirhan A, Törü M, Güler I: Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE journal of biomedical and health informatics 19(4):1451–1458, 2015CrossRefPubMedGoogle Scholar
  29. 29.
    Doyle S, Vasseur F, Dojat M, Forbes F: Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM. Procs. NCI-MICCAI BraTS:18–22, 2013Google Scholar
  30. 30.
    Pereira, S., Festa, J., Mariz, J. A., Sousa, N., & Silva, C. A. (2013). Automatic brain tissue segmentation of multi-sequence MR images using random decision forests. Proceedings of the MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS’13).Google Scholar
  31. 31.
    Geremia, E., Menze, B. H., & Ayache, N. (2012). Spatial decision forests for glioma segmentation in multi-channel MR images. MICCAI Challenge on Multimodal Brain Tumor Segmentation, 34.Google Scholar
  32. 32.
    Guo X, Schwartz L, Zhao B: Semi-automatic segmentation of multimodal brain tumor using active contours. Multimodal Brain Tumor Segmentation 27, 2013Google Scholar
  33. 33.
    Hamamci A, Kucuk N, Karaman K, Engin K, Unal G: Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE transactions on medical imaging 31(3):790–804, 2012CrossRefPubMedGoogle Scholar
  34. 34.
    Meier R, Bauer S, Slotboom J, Wiest R, Reyes M: Appearance-and context-sensitive features for brain tumor segmentation. Proceedings of MICCAI BRATS Challenge:020–026, 2014Google Scholar
  35. 35.
    Reza S, Iftekharuddin KM: Multi-class abnormal brain tissue segmentation using texture. Multimodal Brain Tumor Segmentation 38, 2013Google Scholar
  36. 36.
    Raviv, T. R., Leemput, K. V., & Menze, B. H. (2012, October). Multi-modal brain tumor segmentation via latent atlases. In Proceeding MICCAI-BRATS (pp. 64–73).Google Scholar
  37. 37.
    Shin, H. C. (2012). Hybrid clustering and logistic regression for multi-modal brain tumor segmentation. In Proc. of Workshops and Challanges in Medical Image Computing and Computer-Assisted Intervention (MICCAI’12).Google Scholar
  38. 38.
    Subbanna NK, Precup D, Collins DL, Arbel T: Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes. In: International conference on medical image computing and computer-assisted intervention. Berlin, Heidelberg: Springer, 2013, pp. 751–758Google Scholar
  39. 39.
    Taylor T, John N, Buendia P, Ryan M: Map-reduce enabled hidden Markov models for high throughput multimodal brain tumor segmentation. Multimodal Brain Tumor Segmentation:43, 2013Google Scholar
  40. 40.
    Tustison NJ, Johnson HJ, Rohlfing T, Klein A, Ghosh SS, Ibanez L, Avants B: Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences. Frontiers in neuroscience 7:162, 2013CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Zhao L, Sarikaya D, Corso JJ: Automatic brain tumor segmentation with MRF on supervoxels. Multimodal Brain Tumor Segmentation 51, 2013Google Scholar
  42. 42.
    Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: International conference on medical image computing and computer-assisted intervention. Berlin, Heidelberg: Springer, 2012, October, pp. 369–376Google Scholar
  43. 43.
    Sriramakrishnan P, Kalaiselvi T, Rajeswaran R: Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering 39(2):470–487, 2019CrossRefGoogle Scholar
  44. 44.
    Kalaiselvi, T., Kumarashankar, P., & Sriramakrishnan, (2019). P. Reliability of segmenting brain tumor and finding optimal volume estimator for MR images of patients with glioma’s, International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8, No. 9, pp. 1647–1652.Google Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.School of Computer Science and Technologies, Department of Computer Science and ApplicationsThe Gandhigram Rural Institute (Deemed to be University)GandhigramIndia
  2. 2.School of Computing, Department of Computer ApplicationsKalasalingam Academy of Research and Education (Deemed to be University)KrishnankoilIndia

Personalised recommendations