Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

Artificial Neural Network based Lesion Segmentation of Brain MRI

Tapas Si, Arunava De, Anup Kumar Bhattacharjee. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Tapas Si, Arunava De, Anup Kumar Bhattacharjee
10.5120/cae2016652096

Tapas Si, Arunava De and Anup Kumar Bhattacharjee. Article: Artificial Neural Network based Lesion Segmentation of Brain MRI. Communications on Applied Electronics 4(5):1-5, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Tapas Si and Arunava De and Anup Kumar Bhattacharjee},
	title = {Article: Artificial Neural Network based Lesion Segmentation of Brain MRI},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {5},
	pages = {1-5},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

An Artificial Neural Network based segmentation method for lesion in brain is proposed. First, Magnetic Resonance Images (MRI) are denoised and intensity inhomogeneities are corrected in the preprocessing steps. Artificial neural network is used for training using gray levels and extracted statistical features from the training data with the labelled ground truth. The test images are segmented into lesion and healthy tissues using trained neural network. The connected component labelling algorithm is used to extract only lesion from the segmented images. The proposed method is applied on two MRI data set. The performance of the proposed method is compared with K-means algorithm. The proposed method performs better than K-means algorithm both qualitatively and quantitatively.

References

  1. Tonarelli, L. 2013. Magnetic Resonance Imaging of Brain Tumor, CEwebsource.com.
  2. Parizel P. M., Hauwe L. V. D., Belder F. D., Goethem J. V., Venstermans C., Salgado R., Voormolen M., and Hecke W. V. 2010. Magnetic Resonance Imaging of the Brain, P. Reimer et al. (eds.): Clinical MR Imaging, Springer-Verlag Berlin Heidelberg
  3. Alirezaie J., Jernigan M.E. and Nahmias C. 1997. Neural Network based Segmentation of Magnetic Resonance Images of the Brain, IEEE Transactions on Nuclear Science, 44(3), 194–198
  4. Hyakin S. 2011. Neural Networks and Learning Machines, PHI, 3rd Eds.
  5. Balafar M.A., Ramli A.R. and Mashohor S. 2010. A new method for MR grayscale inhomogeneity correction, Artif. Intell. Rev., 34, 195–204.
  6. Wang S. and Summers R. M. 2012. Machine learning and radiology, Medical Image Analysis, 16, 933–951.
  7. El-Dahshan E. S. A., Mohsen H. M., Revett K. and Salem A. B. M. 2014. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm, Expert Systems with Applications, 41, 5526–5545.
  8. Mayer A. and Greenspan H. 2009. An Adaptive Mean-Shift Framework for MRI Brain Segmentation, IEEE Trans. on Medical Imaging, Vol. 28, No. 8, 1238–1250.
  9. Si T., De A. and Bhattacharjee A.K. 2015. Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI, International Journal of Computer Applications, 121(4), 1–8
  10. Si T., De A. and Bhattacharjee A.K. 2015. Brain’s MRI Segmentation for Lesion Detection using Clustering with Grammatical Swarm Based-Adaptable Particle Swarm Optimizer, Journal of Network and Innovative Computing, MIR Labs, USA, Volume 3, 138–145.
  11. Si T., De A. and Bhattacharjee A.K. 2015. Brain MRI segmentation for tumor detection via entropy maximization using Grammatical Swarm, Int. J. Wavelets Multiresolut Inf. Process., 13(5), 1–8.
  12. Yang M.S., Lin K.C.R, Liu H.C. and Lirng J.F. 2007. Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms, Magnetic Resonance Imaging, 25, Elsevier, 265–277
  13. Zhang N., Ruan S., Lebonvallet S., Liao Q. and Zhu Y. 2011. Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation, Computer Vision and Image Understanding, 115, 256–269.
  14. Ortiz A., Gorriz J. M., Ramirez J., and Salas-Gonzalez D. 2012. Unsupervised Neural Techniques Applied to MR Brain Image Segmentation, Advances in Artificial Neural Systems, Hindawi Publishing Corporation, Volume 2012, Article ID 457590, 7 pages, doi:10.1155/2012/457590
  15. Song T., Jamshidi M.M., Lee R. R. and Huang M. 2007. A Modified Probabilistic Neural Network for Partial Volume Segmentation in Brain MR Image, IEEE Trans. On Neural Networks, Vol. 18, No. 5, 1424–1432.
  16. Zhang Y., Dong Z., Wua L. and Wanga S. 2011. A hybrid method for MRI brain image classification, Expert Systems with Applications, 38, pp. 10049–10053.
  17. Zhang Y., Wang S., Ji G. and Dong Z. 2013. An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine, Hindawi Publishing Corporation The Scientific World Journal, Volume 2013, Article ID 130134, 9 pages, http://dx.doi.org/10.1155/2013/130134
  18. Selvaraj D. and Dhanasekaran R. 2013 A Review on Tissue Segmentation and Feature Extraction of MRI Brain images, International Journal of Computer Science & Engineering Technology, 4(10), 1313–1332
  19. MacQueen J. 1967. Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Math. Stat. Probability, 281–297.
  20. Iftekharuddin K. M., Islam M. A., Shaik J., Parra C. and Ogg R. 2005. Automatic brain tumor detection in MRI: Methodology and statistical validation, SPIE Med. Imag., 5747, 2012–2022.

Keywords

Brain, Magnetic Resonance Image, Lesion, Segmentation, Artificial Neural Network