Published April 1, 2017
| Version 10007245
Journal article
Open
Computer Aided Diagnostic System for Detection and Classification of a Brain Tumor through MRI Using Level Set Based Segmentation Technique and ANN Classifier
Creators
Description
Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.
Files
10007245.pdf
Files
(1.1 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:555320e9c3f13330045bbd7912ddd1f7
|
1.1 MB | Preview Download |
Additional details
References
- The Essential Guide to Brain Tumors, National Brain Tumor Society, http://www.braintumor.org
- A. Drevelegas (ed.), "Imaging of Brain Tumors with Histological Correlations," Springer-Verlag Berlin Heidelberg 2011.
- El-Sayed A. El-Dahshan et al. "Computer-Aided Diagnosis of Human Brain Tumor through MRI: A Survey and a New Algorithm," Expert Systems with Applications, 41 (2014), 5526–5545.
- Jainy Sachdeva et al. "A Novel Content-Based Active Contour Model for Brain Tumor Segmentation," Magnetic Resonance Imaging, 30 (2012), 694–715.
- Stephen M. Smith, "Fast Robust Automated Brain Extraction," Human Brain Mapping, 17:143–155(2002).
- K. Somasundaram, T. Kalaiselvi, "Automatic Brain Extraction Methods for T1 Magnetic Resonance Images Using Region Labeling and Morphological Operations," Computers in Biology and Medicine, 41 (2011), 716–725.
- Jiang et al. "Brain Extraction from Cerebral MRI Volume using a Hybrid Level Set Based Active Contour Neighborhood Model," BioMedical Engineering OnLine 2013, 12:31, http://www.biomedical-engineering-online.com/content/12/1/31
- Gordillo N et al. "State of the Art Survey on MRI Brain Tumor Segmentation," Magnetic Resonance Imaging, 31 (2013), 1426–1438.
- Nobuyuki Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, VOL. SMC-9, NO.1, January 1979. [10] Dubey et al. "Region Growing for MRI Brain Tumor Volume Analysis," Indian Journal of Science and Technology, Vol.2 No. 9 (Sep 2009). [11] Jafari, M, Kasaei, S, "Automatic Brain Tissue Detection in MRI Images Using Seeded Region Growing Segmentation and Neural Network Classification," Australian Journal of Basic and Applied Sciences, 5(8), 1066–1079. [12] Chunming Li et al. "A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with Application to MRI," IEEE Transactions on Image Processing, VOL. 20, NO. 7, JULY 2011. [13] Michael Kass et al. "Snakes: Active contour models," International Journal of Computer Vision, Volume 1, Issue 4, pp 321-331, January 1988. [14] Matthew C. Clark et al. "Automatic Tumor Segmentation Using Knowledge-Based Techniques," IEEE Transactions on Medical Imaging, Vol. 17, No. 2, April 1998. [15] Gudrun Wagenknecht et al. "Knowledge-based Segmentation of Attenuation-relevant Regions of the Head in T1-weighted MR Images for Attenuation Correction in MR/PET Systems," IEEE Nuclear Science Symposium Conference Record, M09-287, 2009. [16] Nara M. Portela et al. "Semi-Supervised Clustering for MR Brain Image Segmentation," Expert Systems with Applications, 41 (2014) 1492–1497. [17] Sanjay Agrawal et al. "A Study on Fuzzy Clustering for Magnetic Resonance Brain Image Segmentation Using Soft Computing Approaches," Applied Soft Computing, 24(2014), 522–533. [18] D.Jude hemanthl et al. "Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation," IEEE International Advance Computing Conference (IACC 2009), Patiala, India, 6-7 March 2009. [19] P. Moallem et al. "Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization," http://www.researchgate.net/publication/233814424 [20] Osher, S., and Sethian J.A., "Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations," Journal of Computational Physics, 79, pp.12-49, (1988). [21] Vicent Caselles et al. "Geodesic Active Contours," International Journal of Computer Vision, 22(1), 61–79 (1997). [22] O. Michailovich, Y. Rathi, A. Tannenbaum, "Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient flow," IEEE Trans. Image Process. 15 (11) (2007) 2787–2801. [23] Hongzhe Yang et al. "Brain Tumor Segmentation Using Geodesic Region-based Level Set without Re-initialization," International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.7, No.1 (2014), pp.213-224. [24] Tony F. Chan, Luminita A. Vese, "Active Contours Without Edges," IEEE Transactions on Image Processing, Vol. 10, No. 2, February 2001. [25] Xiaomin Xie et al. "A Robust Level Set Method Based on Local Statistical Information for Noisy Image Segmentation," Optik 125 (2014) 2199–2204, http://dx.doi.org/10.1016/j.ijleo.2013.10.026 [26] Kiran Thapaliya et al. "Level Set Method with Automatic Selective Local Statistics for Brain Tumor Segmentation in MR Images," Computerized Medical Imaging and Graphics, 37 (2013) 522– 537. [27] Sandeep Chaplot et al. "Classification of Magnetic Resonance Brain Images Using Wavelets as Input to Support Vector Machine and Neural Network," Biomedical Signal Processing and Control, 1 (2006) 86–92. [28] Dipali M. Joshi et al. "Classification of Brain Cancer Using Artificial Neural Network," 2nd International Conference on Electronic Computer Technology (ICECT 2010). [29] http://med.harvard.edu/ANNALIB [30] Mark Sussman et al. "A Level Set Approach for Computing Solutions to Incompressible Two Phase Flow," Journal of Computational Physics, 114, 146-159, (1994). [31] Harlick et al. "Textural Features for Image Classification", IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, No-6, November 1973, pp. 610-621. [32] Martin Fodslette Moller, "Scaled Conjugate Gradient Algorithm for Fast Supervised Learning," Neural Networks, Vol. 6, pp. 525-533, 1993.