Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.538035
Title: Rapid and automatic detection of brain tumors in MR images
Authors: Wang, Z.
Hu, Q.
Loe, K. 
Aziz, A.
Nowinski, W.L.
Keywords: Brain
Correlation coefficient
Detection
Integral of absolute difference
Midsagittal plane
MRI
Root mean square error
Tumor
Variational distance
Issue Date: 2004
Citation: Wang, Z., Hu, Q., Loe, K., Aziz, A., Nowinski, W.L. (2004). Rapid and automatic detection of brain tumors in MR images. Proceedings of SPIE - The International Society for Optical Engineering 5369 : 602-612. ScholarBank@NUS Repository. https://doi.org/10.1117/12.538035
Abstract: An algorithm to automatically detect brain tumors in MR images is presented. The key concern is speed in order to process efficiently large brain image databases and provide quick outcomes in clinical setting. The method is based on study of asymmetry of the brain. Tumors cause asymmetry of the brain, so we detect brain tumors in 3D MR images using symmetry analysis of image grey levels with respect to the midsagittal plane (MSP). The MSP, separating the brain into two hemispheres, is extracted using our previously developed algorithm. By removing the background pixels, the normalized grey level histograms are calculated for both hemispheres. The similarity between these two histograms manifests the symmetry of the brain, and it is quantified by using four symmetry measures: correlation coefficient, root mean square error, integral of absolute difference (IAD), and integral of normalized absolute difference (INAD). A quantitative analysis of brain normality based on 42 patients with tumors and 55 normals is presented. The sensitivity and specificity of IAD and INAD were 83.3% and 89.1%, and 85.7% and 83.6%, respectively. The running time for each symmetry measure for a 3D 8bit MR data was between 0.1 - 0.3 seconds on a 2.4GHz CPU PC.
Source Title: Proceedings of SPIE - The International Society for Optical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/40080
ISSN: 0277786X
DOI: 10.1117/12.538035
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