Symmetry Identification Using Partial Surface Matching and Tilt Correction in 3D Brain Images

We propose a novel method to automatically compute the symmetry plane and correct the 3D orientation of patient brain images. Many images of the brain are clinically unreadable because of the misalignment of the patient's head in the scanner. We proposed an algorithm that represents the brain volume as a re-parameterized surface point cloud where each location has been parameterized by its elevation (latitude), azimuth (longitude) and radius. The removal of the interior contents of the brain makes this approach perform robustly in the presence of the brain pathologies, e.g. tumor, stroke and bleed. Thus, we decompose the symmetry plane computation problem into a surface matching routine. The search for the best matching surface is implemented in a multi-resolution paradigm so as to decrease computational time considerably. Spatial affine transform then is performed to rotate the 3D brain images and align them within the coordinate system of the scanner. The corrected brain volume is re-sliced such that each planar image represents the brain at the same axial level

Brain symmetry plane computation in MR images using inertia axes and optimization

International audienceDetection of the best symmetry plane in 3D images can be treated as a registration problem between the original and the reflected images. The registration is performed in a 3D space of parameters defining orientation and shift of a reflection plane. We use the normalized ¦ £ metric as the similarity measure between original and reflected images and investigate an algorithm for computation of the best symmetry plane. The algorithm computes first an initial position of the plane by analyzing principal inertia axes. We demonstrate on several MR brain images that the initial position is in the neighborhood of the global maximum. Therefore the downhill simplex method is further used for the computation of the best symmetry plane. The proposed algorithm was tested on simulated and real MR brain images

Software para el estudio del volumen de estructuras corticales en imágenes de RMN cerebrales

El estudio del volumen intracraneal requiere del uso de herramientas que permitan objetivar el diagnóstico y ofrezcan un rendimiento y precisión elevados. La segmentación automática del volumen cerebral es el primer paso hacia un estudio más completo del cerebro y supondrá una herramienta versátil en el estudio de diversas patologías. Propósito: Mejorar un método ya implementado que segmenta el volumen cerebral con una calidad aceptable pero en un tiempo de ejecución elevado basado en comparación de regiones contra una biblioteca de casos de ejemplo segmentada manualmente. Método: El método recorre uno a uno todos los voxels del cerebro a segmentar extrayendo la región que lo envuelve y comparándola con regiones de los casos de ejemplo de la biblioteca. Esto era ineficiente así que se han introducido mejoras que van desde la carga y preselección de los casos más semejantes para usarlos en la segmentación hasta introducir una estimación pre calculada del etiquetado de los voxels que no suelen variar para evitar tener que procesarlos. Resultados: Se parte de un método que obtiene segmentaciones con una 98% de fiabilidad y en un tiempo de ejecución de 160 segundos y se ha mejorado hasta una fiabilidad del 99% en un tiempo inferior a 40 segundos.Romero Gómez, JE. (2011). Software para el estudio del volumen de estructuras corticales en imágenes de RMN cerebrales. http://hdl.handle.net/10251/14239.Archivo delegad

Automatic quantification of brain midline shift in CT images

Ph.DDOCTOR OF PHILOSOPH

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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Proc., pp. 92-99Junck, L., Moen, J.G., Hutchins, G.D., Brown, M.B., Kuhl, D.E., Correlation methods for the centering, rotation, and alignment of functional brain images (1990) The Journal of Nuclear Medicine, 31, pp. 1220-1226Minoshima, S., Berger, K.L., Lee, K.S., Mintun, M.A., An automated method for rotational correction and centering of three-dimensional functional brain images (1992) The Journal of Nuclear Medicine, 33, pp. 1579-1585Sun, C., Sherrah, J., 3D symmetry detection using the extended Gaussian image (1997) IEEE Trans. on Pattern Analysis and Machine Intelligence, 19, pp. 164-168Smith, S.M., Jenkinson, M., Accurate robust symmetry estimation (1999) LNCS, 1679, pp. 308-317. , Taylor, C., Colchester, A. (eds.) MICCAI 1999. Springer, HeidelbergPrima, S., Ourselin, S., Ayache, N., Computation of the mid-sagittal plane in 3D brain images (2002) IEEE Trans. on Medical Imaging, 21, pp. 122-138Tuzikov, A.V., Colliot, O., Bloch, I., Evaluation of the symmetry plane in 3D MR brain images (2003) Pattern Recognition Letters, 24, pp. 2219-2233Teverovskiy, L., Liu, Y., Truly 3D midsagittal plane extraction for robust neuroimage registration (2006) Proc. of 3rd IEEE Intl. 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