958 resultados para micro-CT,cone beam Ct,trabecular tissue,image segmentation,computed tomography
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Aim: To determine the prevalence and classification of bifid mandibular canals using cone beam computed tomography (CBCT). Methods: The sample comprised 300 CBCT scans obtained from the Radiology and Imaging Department database at São Leopoldo Mandic Dental School, Campinas, SP, Brazil. All images were performed on Classic I-Cat® CBCT scanner, with standardized voxel at 0.25 mm and 13 cm FOV (field of view). From an axial slice (0.25 mm) a guiding plane was drawn along the alveolar ridge in order to obtain a cross-section. Results: Among 300 patients, 188 (62.7%) were female and 112 (37.3%) were male, aged between 13 to 87 years. Changes in the mandibular canal were observed in 90 patients, 30.0% of the sample, 51 women (56.7%) and 39 men (43.3%). Regarding affected sides, 32.2% were on the right and 24.5% on the left, with 43.3% bilateral cases. Conclusions: According to the results obtained in this study, a prevalence of 30% of bifid mandibular canals was found, with the most prevalent types classified as B (mesial direction) and bilateral.
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This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.
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We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.
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Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.
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AbstractObjective:To report the results of computed tomography (CT)-guided percutaneous resection of the nidus in 18 cases of osteoid osteoma.Materials and Methods:The medical records of 18 cases of osteoid osteoma in children, adolescents and young adults, who underwent CT-guided removal of the nidus between November, 2004 and March, 2009 were reviewed retrospectively for demographic data, lesion site, clinical outcome and complications after procedure.Results:Clinical follow-up was available for all cases at a median of 29 months (range 6–60 months). No persistence of pre-procedural pain was noted on 17 patients. Only one patient experienced recurrence of symptoms 12 months after percutaneous resection, and was successfully retreated by the same technique, resulting in a secondary success rate of 18/18 (100%).Conclusion:CT-guided removal or destruction of the nidus is a safe and effective alternative to surgical resection of the osteoid osteoma nidus.
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Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.
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We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.
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Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.
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This paper considers the problem of tissue classification in 3D MRI. More specifically, a new set of texture features, based on phase information, is used to perform the segmentation of the bones of the knee. The phase information provides a very good discrimination between the bone and the surrounding tissues, but is usually not used due to phase unwrapping problems. We present a method to extract textural information from the phase that does not require phase unwrapping. The textural information extracted from the magnitude and the phase can be combined to perform tissue classification, and used to initialise an active shape model, leading to a more precise segmentation.
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Recently, stress myocardial computed tomographic perfusion (CTP) was shown to detect myocardial ischemia. Our main objective was to evaluate the feasibility of dipyridamole stress CTP and compare it to single-photon emission computed tomography (SPECT) to detect significant coronary stenosis using invasive conventional coronary angiography (CCA; stenosis >70%) as the reference method. Thirty-six patients (62 +/- 8 years old, 20 men) with previous positive results with SPECT (<2 months) as the primary inclusion criterion and suspected coronary artery disease underwent a customized multidetector-row CT protocol with myocardial perfusion evaluation at rest and during stress and coronary CT angiography (CTA). Multidetector-row computed tomography was performed in a 64-slice scanner with dipyridamole stress perfusion acquisition before a second perfusion/CT angiographic acquisition at rest. Independent blinded observers performed analysis of images from CTP, CTA, and CCA. All 36 patients completed the CT protocol with no adverse events (mean radiation dose 14.7 +/- 3.0 mSv) and with interpretable scans. CTP results were positive in 27 of 36 patients (75%). From the 9 (25%) disagreements, 6 patients had normal coronary arteries and 2 had no significant stenosis (8 false-positive results with SPECT, 22%). The remaining patient had an occluded artery with collateral flow confirmed by conventional coronary angiogram. Good agreement was demonstrated between CTP and SPECT on a per-patient analysis (kappa 0.53). In 26 patients using CCA as reference, sensitivity, specificity, and positive and negative predictive values were 88.0%, 79.3%, 66.7%, and 93.3% for CTP and 68.8, 76.1%, 66.7%, and 77.8%, for SPECT, respectively (p = NS). In conclusion, dipyridamole CT myocardial perfusion at rest and during stress is feasible and results are similar to single-photon emission CT scintigraphy. The anatomical-perfusion information provided by this combined CT protocol may allow identification of false-positive results by SPECT. (C) 2010 Elsevier Inc. All rights reserved. (Am J Cardiol 2010;106:310-315)
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Introduction: The aim of this study was to evaluate the root canal preparation in flat-oval canals treated with either rotary or self-adjusting file (SAF) by using micro-tomography analysis. Methods: Forty mandibular incisors were scanned before and after root canal instrumentation with rotary instruments (n = 20) or SAF (n = 20). Changes in canal volume, surface area, and cross-sectional geometry were compared with preoperative values. Data were compared by independent sample t test and chi(2) test between groups and paired sample t test within the group (alpha = 0.05). Results: Overall, area, perimeter, roundness, and major and minor diameters revealed no statistical difference between groups (P > .05). In the coronal third, percentage of prepared root canal walls and mean increases of volume and area were significantly higher with SAF (92.0%, 1.44 +/- 0.49 mm(3), 0.40 +/- 0.14 mm(2), respectively) than rotary instrumentation (62.0%, 0.81 +/- 0.45 mm(3), 0.23 +/- 0.15 mm2, respectively) (P < .05). SAF removed dentin layer from all around the canal, whereas rotary instrumentation showed substantial untouched areas. Conclusions: In the coronal third, mean increases of area and volume of the canal as well as the percentage of prepared walls were significantly higher with SAF than with rotary instrumentation. By using SAF instruments, flat-oval canals were homogenously and circumferentially prepared. The size of the SAF preparation in the apical third of the canal was equivalent to those prepared with #40 rotary file with a 0.02 taper. (J Endod 2011;37:1002-1007)
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In this thesis a semi-automated cell analysis system is described through image processing. To achieve this, an image processing algorithm was studied in order to segment cells in a semi-automatic way. The main goal of this analysis is to increase the performance of cell image segmentation process, without affecting the results in a significant way. Even though, a totally manual system has the ability of producing the best results, it has the disadvantage of taking too long and being repetitive, when a large number of images need to be processed. An active contour algorithm was tested in a sequence of images taken by a microscope. This algorithm, more commonly known as snakes, allowed the user to define an initial region in which the cell was incorporated. Then, the algorithm would run several times, making the initial region contours to converge to the cell boundaries. With the final contour, it was possible to extract region properties and produce statistical data. This data allowed to say that this algorithm produces similar results to a purely manual system but at a faster rate. On the other hand, it is slower than a purely automatic way but it allows the user to adjust the contour, making it more versatile and tolerant to image variations.
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BACKGROUND: Recanalization in acute ischemic stroke with large-vessel occlusion is a potent indicator of good clinical outcome. OBJECTIVE: To identify easily available clinical and radiologic variables predicting recanalization at various occlusion sites. METHODS: All consecutive, acute stroke patients from the Acute STroke Registry and Analysis of Lausanne (2003-2011) who had a large-vessel occlusion on computed tomographic angiography (CTA) (< 12 h) were included. Recanalization status was assessed at 24 h (range: 12-48 h) with CTA, magnetic resonance angiography, or ultrasonography. Complete and partial recanalization (corresponding to the modified Treatment in Cerebral Ischemia scale 2-3) were grouped together. Patients were categorized according to occlusion site and treatment modality. RESULTS: Among 439 patients, 51% (224) showed complete or partial recanalization. In multivariate analysis, recanalization of any occlusion site was most strongly associated with endovascular treatment, including bridging therapy (odds ratio [OR] 7.1, 95% confidence interval [CI] 2.2-23.2), and less so with intravenous thrombolysis (OR 1.6, 95% CI 1.0-2.6) and recanalization treatments performed beyond guidelines (OR 2.6, 95% CI 1.2-5.7). Clot location (large vs. intermediate) and tandem pathology (the combination of intracranial occlusion and symptomatic extracranial stenosis) were other variables discriminating between recanalizers and non-recanalizers. For patients with intracranial occlusions, the variables significantly associated with recanalization after 24 h were: baseline National Institutes of Health Stroke Scale (NIHSS) (OR 1.04, 95% CI 1.02-1.1), Alberta Stroke Program Early CT Score (ASPECTS) on initial computed tomography (OR 1.2, 95% CI 1.1-1.3), and an altered level of consciousness (OR 0.2, 95% CI 0.1-0.5). CONCLUSIONS: Acute endovascular treatment is the single most important factor promoting recanalization in acute ischemic stroke. The presence of extracranial vessel stenosis or occlusion decreases recanalization rates. In patients with intracranial occlusions, higher NIHSS score and ASPECTS and normal vigilance facilitate recanalization. Clinical use of these predictors could influence recanalization strategies in individual patients.
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In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach
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In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram