Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14130
Title: Brain tumor detection from 3D magnetic resonance images
Authors: WANG ZHENGJIA
Keywords: Brain, pathology detection, midsagittal plane, magnetic resonance imaging (MRI), variational distance, divergence measure
Issue Date: 20-Aug-2004
Source: WANG ZHENGJIA (2004-08-20). Brain tumor detection from 3D magnetic resonance images. ScholarBank@NUS Repository.
Abstract: An algorithm to automatically detect brain tumors from magnetic resonance (MR) images is presented. The method is based on study of asymmetry of the brain. A healthy human brain is roughly symmetrical bilaterally. Changes in the relative shape and structure of two hemispheres are considered as a sign of abnormality. We use five symmetry measures: correlation coefficient (CC), root mean square error (RMSE), integral of absolute difference (IAD), integral of normalized absolute difference (INAD), and informational divergence measure J-divergence (JD) to calculate similarity between image intensity distributions corresponding to the two hemispheres with respect to the midsagittal plane (MSP) to estimate brain asymmetry. Abnormality of brain is validated on 168 studies in 101 patients (42 tumors and 59 normal). The sensitivity and specificity of IAD, INAD and JD were 83.3% and 89.1%, 85.7% and 83.6%, and 83% and 92%, respectively. The value of empirical thresholds between the normal and tumor datasets for IAD, INAD and J-D are 0.0655, 23.00, and 0.0081 respectively. The method is MRI pulse sequence independent and computationally effective, running in less than 0.3 seconds on Pentium 4 (2.4GHz, Standard PC) for one brain MRI study.
URI: http://scholarbank.nus.edu.sg/handle/10635/14130
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