1. bookVolume 13 (2013): Issue 5 (October 2013)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Open Access

Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

Published Online: 02 Nov 2013
Volume & Issue: Volume 13 (2013) - Issue 5 (October 2013)
Page range: 223 - 230
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

This work focuses on fully automatic detection of brain tumors. The first aim is to determine, whether the image contains a brain with a tumor, and if it does, localize it. The goal of this work is not the exact segmentation of tumors, but the localization of their approximate position. The test database contains 203 T2-weighted images of which 131 are images of healthy brain and the remaining 72 images contain brain with pathological area. The estimation, whether the image shows an afflicted brain and where a pathological area is, is done by multi resolution symmetry analysis. The first goal was tested by five-fold cross-validation technique with 100 repetitions to avoid the result dependency on sample order. This part of the proposed method reaches the true positive rate of 87.52% and the true negative rate of 93.14% for an afflicted brain detection. The evaluation of the second part of the algorithm was carried out by comparing the estimated location to the true tumor location. The detection of the tumor location reaches the rate of 95.83% of correct anomaly detection and the rate 87.5% of correct tumor location.

Keywords

[1] Dvorak, P., Kropatsch, W.G., Bartusek, K. (2013). Pathological area detection in MR images of brain. Elektrorevue, 4(1), 17-21.Search in Google Scholar

[2] Cuadra, M.B., Pollo, C., Bardera, A., Cuisenaire, O., Villemure, J.G., Thiran, J.P. (2004). Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Transactions on Medical Imaging, 23(1), 1301-1314.Search in Google Scholar

[3] Cap, M., Marcon, P., Gescheidtova, E., Bartusek, K. (2013). Automatic detection and segmentation of the tumor tissue. In Proceedings of PIERS 2013, Taipei, 53-56.Search in Google Scholar

[4] Pedoia, V., Binaghi, E., Balbi, S., De Benedictis, A., Monti, E., Minotto, R. (2012). Glial brain tumor detection by using symmetry analysis. In Proceedings of SPIE, Volume 8314, Medical Imaging 2012: Image Processing, 831445.Search in Google Scholar

[5] Somasundaram, K., Kalaiselvi, T., (2010). Automatic detection of brain tumor from MRI scans using maxima transform. In National Conference on Image Processing (NCIMP).Search in Google Scholar

[6] Mikulka, J., Gescheidtova, E. (2013). An improved segmentation of brain tumor, edema and necrosis. In Proceedings of PIERS 2013, Taipei, 25-28.Search in Google Scholar

[7] Capelle, A.S., Color, O., Fernandez-Maloigne, C. (2004). Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information. Information Fusion, 5, 103-216.10.1016/j.inffus.2003.10.001Search in Google Scholar

[8] Karuppanagounder, S., Thiruvenkadam, K. (2009). A novel technique for finding the boundary between the cerebral hemispheres from MR axial head scans. In Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009 Tumkur, Karnataka, India, December 16-18, 1486-1502.Search in Google Scholar

[9] Ruppert, G.C.S., Teverovskiy, L., Yu, C.-P., Falcao, A.X., Liu, Y. (2011). A new symmetry-based method for mid-sagittal plane extraction in neuroimages. In IEEE International Symposium on Biomedical Imaging: From Macro to Nano.Search in Google Scholar

[10] Ray, N., Saha, B.N., Graham Brown, M.R. (2007). Locating brain tumors from MR imagery using symmetry. In The Forty-First Asilomar Conference on Signals, Systems and Computers, ACSSC 2007, 224-228.10.1109/ACSSC.2007.4487200Search in Google Scholar

[11] Mikulka, J., Gescheidtova, E., Bartusek, K. (2012). Soft-tissues image processing: Comparison of tradi- tional segmentation methods with 2D active contour methods. Measurement Science Review, 12(4), 153-161.10.2478/v10048-012-0023-8Search in Google Scholar

[12] Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by their probability distribution. In Bulletin of the Calcutta Mathematical Society,35, 99-110.Search in Google Scholar

[13] Kropatsch, W.G., Haxhimusa, Y., Ion, A. (2007). Multiresolution image segmentations in graph pyramids. In Applied Graph Theory in Computer Vision and Pattern Recognition Studies in Computational Intelligence, 52, 3-41.10.1007/978-3-540-68020-8_1Search in Google Scholar

[14] Cortes, C., Vapnik, V.N. (1995). Support-Vector Networks. Machine Learning, 20(3), 271-297.10.1007/BF00994018Search in Google Scholar

[15] Jaccard, P. (1908). Nouvelles recherches sur la distribution florale. Bulletin de la Société Vaudoise des Sciences Naturelles, 44, 223-270. Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo