Structural and radiometric asymmetry in brain images

Joshi, Sarang; Lorenzen, Peter; Gerig, Guido; Bullitt, Elizabeth

doi:10.1016/s1361-8415(03)00002-1

Cited by 41 publications

(24 citation statements)

References 14 publications

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“…The region of interest is restricted to the pelvis as it remains fixed while the femurs and spine can rotate or bend. The rigid transformation, r, is estimated using an intensity based gradient descent algorithm [8].…”

Section: Methodsmentioning

confidence: 99%

Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate

Davis¹,

Foskey²,

Rosenman³

et al. 2005

Lecture Notes in Computer Science

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Abstract.We have been developing an approach for automatically quantifying organ motion for adaptive radiation therapy of the prostate. Our approach is based on deformable image registration, which makes it possible to establish a correspondence between points in images taken on different days. This correspondence can be used to study organ motion and to accumulate inter-fraction dose. In prostate images, however, the presence of bowel gas can cause significant correspondence errors. To account for this problem, we have developed a novel method that combines large deformation image registration with a bowel gas segmentation and deflation algorithm. In this paper, we describe our approach and present a study of its accuracy for adaptive radiation therapy of the prostate. All experiments are carried out on 3-dimensional CT images.

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Section: Methodsmentioning

confidence: 99%

Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate

Davis¹,

Foskey²,

Rosenman³

et al. 2005

Lecture Notes in Computer Science

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“…16 However, the ratio of grey to white matter is smaller in the right hemisphere than in the left, 17 and numerous regional interhemispheric asymmetries have also been identified. [17][18][19][20][21] Of these inter-hemispheric differences, regions that the CC fibres are likely to cross include frontal areas (where the right volume is greater than the left) and occipital areas (where the left volume is greater than the right).…”

Section: Discussionmentioning

confidence: 99%

A semi-automated method for measuring thickness and white matter integrity of the corpus callosum

2012

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Many diseases affect human brain white matter, multifocally or diffusely, in adults and children. These may be metabolic (e.g. Leigh's disease), toxic (e.g. carbon monoxide poisoning), hypoxic (e.g. hypoxic ischaemic injury), infective (e.g. HIV or syphilis), inflammatory (e.g. multiple sclerosis) or vascular (e.g. microvascular dementia). Two important conditions that result in significant loss of global white matter volume in affected children are hypoxic ischaemic injury and HIV encephalopathy. These diseases result in the thinning of associated fibre bundles and may be reflected by atrophy of inter-hemispheric connections -most importantly the corpus callosum (CC) (Fig. 1).A simple automated method of determining normal values of the CC according to age is desirable. A tool which is also applicable for diagnosing white matter disorders and offering prognosis in children with global white matter insults will have a wide clinical application. This article describes a semi-automated system that divides the midline CC into a number of segments, determines thickness at each, and performs fibre tracking from these segments. Aim.Diseases affecting cerebral white matter may lead to left-right asymmetries and atrophy of interhemispheric connections, i.e. the corpus callosum (CC). Our aim was to describe and test a semi-automated system that divides the midline CC into a number of segments and determines thickness at each, then performs fibre tracking from these segments. Methods. Six normal female volunteers (average age 25.8 ± 6.7 years) and a female patient with diagnosed multiple sclerosis (age 26 years) were scanned on a 3T MRI. We performed diffusion-weighted imaging in 12 directions, and calculated diffusion tensors and fractional anisotropy (FA) maps from this pre-processed data. Fibre tracking from a region-of-interest encompassing the entire CC was done. This fibre data, together with FA maps and the unweighted diffusion tensor imaging (DTI) image (b = 0 s/mm 2 ), were imported into a custom tool written in MATLAB. The midline sagittal position was carefully defined by selecting multiple midline points in coronal and axial views and rotating the image volume and fibre co-ordinates accordingly.Using the customised tool, dorsal and ventral CC contours were manually drawn on the mid-sagittal FA image, initiating automated calculation of a contour midway between these manually drawn lines. The programme was designed to then divide the midline contour into a pre-selected number of segments; from each segment border, perpendicular spokes were projected until they intersected with the dorsal and ventral contours. This technique divided the CC into a pre-set amount of segments, the number of which was limited by the spatial resolution. It was decided to set the number at 40 to ensure that each segment depicted a contiguous strip of voxels across the CC from the dorsal to the ventral contour. The system allows these segments to then be used as seeds for separate fibre tracking in each cerebral hemisphere, and various...

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“…However, each set of slices may differ from other sets in term of the angle of scanning (pitch) and due to the fact that the patient's head may be tilted during the scanning process, resulting in an off-centered set of slices. To overcome these drawbacks, ROIs used in our study were placed in brain images so as to ensure a best reflectional symmetry passing through the mid-sagittal plane of the brain [14], [15]. This is done by finding the orientation angles Ɵ.…”

Section: Image Featuresmentioning

confidence: 99%

Hemorrhage detection in MRI brain images using images features

Moraru¹,

Moldovanu²,

Bibicu³

et al. 2013

AIP Conference Proceedings

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Abstract. The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid -sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

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Structural and radiometric asymmetry in brain images

Cited by 41 publications

References 14 publications

Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate

Automatic Segmentation of Intra-treatment CT Images for Adaptive Radiation Therapy of the Prostate

A semi-automated method for measuring thickness and white matter integrity of the corpus callosum

Hemorrhage detection in MRI brain images using images features

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