AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT

Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window selection algorithm (WSA) is applied first to confine the region of interest, then the variational level set method is used to segment the image and extract the ventricle contours. With a ventricle identification algorithm (VIA), the position of actual midline is detected based on the identified right and left lateral ventricle contours. Finally, the brain midline shift is calculated using the positions of detected ideal midline and actual midline. One of the important applications of midline shift in clinical medical decision making is to estimate the intracranial pressure (ICP). ICP monitoring is a standard procedure in the care of severe traumatic brain injury (TBI) patients. An automated ICP level prediction model based on machine learning method is proposed in this work. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are used in the ICP level prediction. Finally, the results are evaluated to assess the effectiveness of the proposed method in ICP level prediction

A Fast And Automatic Method For 3d Rigid Registration Of Mr Images Of The Human Brain

Image registration is an important problem with several applications in Medical Imaging. Intra-subject rigid registration requires a minimal set of parameters to be computed, and is sufficient for organs with no significant movement or deformation, such as the human brain. Rigid registration has also been used as the first step before inter-subject deformable registration. In this paper we present a fast and automatic method for 3D rigid registration of magnetic resonance images of the human brain. The method combines previous approaches for mid-sagittal plane location and brain segmentation with a greedy-search algorithm to find the best match between source and target images. We evaluated the method on 200 image pairs: 100 without structural abnormalities and 100 with artificially created lesions, such that it was possible to quantify the registration errors. The method achieved very accurate registration within a few seconds. © 2008 IEEE.121128Audette, M., Ferrie, F., Peters, T., An algorithmic overview of surface registration techniques for medical imaging (2000) Medical Image Analysis, 4 (3), pp. 201-217Bergo, F.P.G., Falcão, A.X., Miranda, P.A.V., Rocha, L.M., Automatic image segmentation by tree pruning (2007) J Math Imaging and Vision, 29 (2-3), pp. 141-162. , NovF. P. G. Bergo, G. C. S. Ruppert, L. F. Pinto, and A. X. Falcão. Fast and robust mid-sagittal plane location in 3D MR images of the brain. In Proc. BIOSIGNALS 2008 - Intl. Conf. on Bio-Inspired Syst. and Sig. Proc., pages 92-99, Jan 2008Besl, P.J., McKay, N.D., A method for registration of 3-d shapes (1992) IEEE Transactions on pattern analysis and machine intelligence, 14 (2), pp. 239-256Brown, L.G., A survey of image registration techniques (1992) ACM. 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L. Yasuda, C. C. Valise, A. V. Saúde, A. L. F. Costa, F. R. Pereira, M. Morita, L. E. Betting, H. Tedeschi, E. Oliveira, G. Castellano, and F. Cendes. Recovery of white matter atrophy (WMA) after successful surgery in mesial TLE. In Proc. 60th Annual Meeting of the American Academy of Neurology, page to appear, 2008Zhou, H., Liu, T., Lin, F., Pang, Y., Wu, J., Wu, J., Towards efficient registration of medical images (2007) Computerized Medical Imaging and Graphics, 31, pp. 374-382. , SepZivota, B., Flusser, J., Image registration methods: A survey (2003) Image and Vision Computing, 21, pp. 977-1000Zou, X., Zhao, X., Feng, Y., An efficient medical image registration algorithm based on gradient descent (2007) Complex Medical Engineering, pp. 636-639. , Ma

Fast, Accurate And Precise Mid-sagittal Plane Location In 3d Mr Images Of The Brain

Extraction of the mid-sagittal plane (MSP) is a key step for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, based on automatic segmentation of the brain and on heuristic maximization of the cerebro-spinal fluid within the MSP. The method is robust to severe anatomical asymmetries between the hemispheres, caused by surgical procedures and lesions. The method is also accurate with respect to MSP delineations done by a specialist. The method was evaluated on 64 MR images (36 pathological, 20 healthy, 8 synthetic), and it found a precise and accurate approximation of the MSP in all of them with a mean time of 60.0 seconds per image, mean angular variation within a same image (precision) of 1.26° and mean angular difference from specialist delineations (accuracy) of 1.64°. © 2008 Springer-Verlag.25 CCIS278290Davidson, R.J., Hugdahl, K., (1996) Brain Asymmetry, , MIT Press/Bradford BooksCrow, T.J., Schizophrenia as an anomaly of cerebral asymmetry (1993) Imaging of the Brain in Psychiatry and Related Fields, pp. 3-17. , Maurer, K. (ed.) 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