916 resultados para Morphing Alteration Detection Image Warping


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To assess the sensitivity and image quality of chest radiography (CXR) with or without dual-energy subtracted (ES) bone images in the detection of rib fractures.

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Accurate placement of lesions is crucial for the effectiveness and safety of a retinal laser photocoagulation treatment. Computer assistance provides the capability for improvements to treatment accuracy and execution time. The idea is to use video frames acquired from a scanning digital ophthalmoscope (SDO) to compensate for retinal motion during laser treatment. This paper presents a method for the multimodal registration of the initial frame from an SDO retinal video sequence to a retinal composite image, which may contain a treatment plan. The retinal registration procedure comprises the following steps: 1) detection of vessel centerline points and identification of the optic disc; 2) prealignment of the video frame and the composite image based on optic disc parameters; and 3) iterative matching of the detected vessel centerline points in expanding matching regions. This registration algorithm was designed for the initialization of a real-time registration procedure that registers the subsequent video frames to the composite image. The algorithm demonstrated its capability to register various pairs of SDO video frames and composite images acquired from patients.

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Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.

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The purpose of this article is to assess the impact of large patient size on the detection of hypovascular liver tumors with MDCT and the effect of a noise filter on image quality and lesion detection in obese patients.

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A new simple method for two-dimensional determination of optical density of macular pigment xanthophyll (ODx) in clinical routine is based on a single blue-reflection fundus image. Individual different vignetting is corrected by a shading function. For its construction, nodes are automatically found in structureless image regions. The influence of stray light in elderly crystalline lenses is compensated by a correction function that depends on age. The reproducibility of parameters in a one-wavelength reflection method determined for three subjects (47, 61, and 78 years old) was: maxODx = 6.3%, meanODx = 4.6%, volume = 6%, and area = 6% already before stray-light correction. ODx was comparable in pseudophakic and in an eye with a crystalline lens of the same 11 subjects after stray-light correction. Significant correlation in ODx was found between the one-wavelength reflection method and the two-wavelength autofluorescence method for pseudophakic and cataract eyes of 19 patients suffering from dry age-related macular degeneration (AMD) (R(2) = 0.855). In pseudophakic eyes, maxODx was significantly lower for dry AMD (n = 45) (ODx = 0.491±0.102 ODU) than in eyes with healthy fundus (n = 22) (ODx = 0.615±0.103 ODU) (p = 0.000033). Also in eyes with crystalline lens, maxODx was lower in AMD (n = 125) (ODx = 0.610±0.093 ODU) than in healthy subjects (n = 45) (ODx = 0.674±0.098 ODU) (p = 0.00019). No dependence on age was found in the pseudophakic eyes both of healthy subjects and AMD patients.

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Statistical models have been recently introduced in computational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of nite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations.

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Craniosynostosis consists of a premature fusion of the sutures in an infant skull that restricts skull and brain growth. During the last decades, there has been a rapid increase of fundamentally diverse surgical treatment methods. At present, the surgical outcome has been assessed using global variables such as cephalic index, head circumference, and intracranial volume. However, these variables have failed in describing the local deformations and morphological changes that may have a role in the neurologic disorders observed in the patients. This report describes a rigid image registration-based method to evaluate outcomes of craniosynostosis surgical treatments, local quantification of head growth, and indirect intracranial volume change measurements. The developed semiautomatic analysis method was applied to computed tomography data sets of a 5-month-old boy with sagittal craniosynostosis who underwent expansion of the posterior skull with cranioplasty. Quantification of the local changes between pre- and postoperative images was quantified by mapping the minimum distance of individual points from the preoperative to the postoperative surface meshes, and indirect intracranial volume changes were estimated. The proposed methodology can provide the surgeon a tool for the quantitative evaluation of surgical procedures and detection of abnormalities of the infant skull and its development.

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A new generation of high definition computed tomography (HDCT) 64-slice devices complemented by a new iterative image reconstruction algorithm-adaptive statistical iterative reconstruction, offer substantially higher resolution compared to standard definition CT (SDCT) scanners. As high resolution confers higher noise we have compared image quality and radiation dose of coronary computed tomography angiography (CCTA) from HDCT versus SDCT. Consecutive patients (n = 93) underwent HDCT, and were compared to 93 patients who had previously undergone CCTA with SDCT matched for heart rate (HR), HR variability and body mass index (BMI). Tube voltage and current were adapted to the patient's BMI, using identical protocols in both groups. The image quality of all CCTA scans was evaluated by two independent readers in all coronary segments using a 4-point scale (1, excellent image quality; 2, blurring of the vessel wall; 3, image with artefacts but evaluative; 4, non-evaluative). Effective radiation dose was calculated from DLP multiplied by a conversion factor (0.014 mSv/mGy × cm). The mean image quality score from HDCT versus SDCT was comparable (2.02 ± 0.68 vs. 2.00 ± 0.76). Mean effective radiation dose did not significantly differ between HDCT (1.7 ± 0.6 mSv, range 1.0-3.7 mSv) and SDCT (1.9 ± 0.8 mSv, range 0.8-5.5 mSv; P = n.s.). HDCT scanners allow low-dose 64-slice CCTA scanning with higher resolution than SDCT but maintained image quality and equally low radiation dose. Whether this will translate into higher accuracy of HDCT for CAD detection remains to be evaluated.

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Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

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The purpose of the study was to evaluate observer performance in the detection of pneumothorax with cesium iodide and amorphous silicon flat-panel detector radiography (CsI/a-Si FDR) presented as 1K and 3K soft-copy images. Forty patients with and 40 patients without pneumothorax diagnosed on previous and subsequent digital storage phosphor radiography (SPR, gold standard) had follow-up chest radiographs with CsI/a-Si FDR. Four observers confirmed or excluded the diagnosis of pneumothorax according to a five-point scale first on the 1K soft-copy image and then with help of 3K zoom function (1K monitor). Receiver operating characteristic (ROC) analysis was performed for each modality (1K and 3K). The area under the curve (AUC) values for each observer were 0.7815, 0.7779, 0.7946 and 0.7066 with 1K-matrix soft copies and 0.8123, 0.7997, 0.8078 and 0.7522 with 3K zoom. Overall detection of pneumothorax was better with 3K zoom. Differences between the two display methods were not statistically significant in 3 of 4 observers (p-values between 0.13 and 0.44; observer 4: p = 0.02). The detection of pneumothorax with 3K zoom is better than with 1K soft copy but not at a statistically significant level. Differences between both display methods may be subtle. Still, our results indicate that 3K zoom should be employed in clinical practice.

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PURPOSE: To prospectively evaluate, for the depiction of simulated hypervascular liver lesions in a phantom, the effect of a low tube voltage, high tube current computed tomographic (CT) technique on image noise, contrast-to-noise ratio (CNR), lesion conspicuity, and radiation dose. MATERIALS AND METHODS: A custom liver phantom containing 16 cylindric cavities (four cavities each of 3, 5, 8, and 15 mm in diameter) filled with various iodinated solutions to simulate hypervascular liver lesions was scanned with a 64-section multi-detector row CT scanner at 140, 120, 100, and 80 kVp, with corresponding tube current-time product settings at 225, 275, 420, and 675 mAs, respectively. The CNRs for six simulated lesions filled with different iodinated solutions were calculated. A figure of merit (FOM) for each lesion was computed as the ratio of CNR2 to effective dose (ED). Three radiologists independently graded the conspicuity of 16 simulated lesions. An anthropomorphic phantom was scanned to evaluate the ED. Statistical analysis included one-way analysis of variance. RESULTS: Image noise increased by 45% with the 80-kVp protocol compared with the 140-kVp protocol (P < .001). However, the lowest ED and the highest CNR were achieved with the 80-kVp protocol. The FOM results indicated that at a constant ED, a reduction of tube voltage from 140 to 120, 100, and 80 kVp increased the CNR by factors of at least 1.6, 2.4, and 3.6, respectively (P < .001). At a constant CNR, corresponding reductions in ED were by a factor of 2.5, 5.5, and 12.7, respectively (P < .001). The highest lesion conspicuity was achieved with the 80-kVp protocol. CONCLUSION: The CNR of simulated hypervascular liver lesions can be substantially increased and the radiation dose reduced by using an 80-kVp, high tube current CT technique.

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OBJECTIVES: To assess magnetic resonance (MR)-colonography (MRC) for detection of colorectal lesions using two different T1w three-dimensional (3D)-gradient-recalled echo (GRE)-sequences and integrated parallel data acquisition (iPAT) at a 3.0 Tesla MR-unit. MATERIALS AND METHODS: In this prospective study, 34 symptomatic patients underwent dark lumen MRC at a 3.0 Tesla unit before conventional colonoscopy (CC). After colon distension with tap water, 2 high-resolution T1w 3D-GRE [3-dimensional fast low angle shot (3D-FLASH), iPAT factor 2 and 3D-volumetric interpolated breathhold examination (VIBE), iPAT 3] sequences were acquired without and after bolus injection of gadolinium. Prospective evaluation of MRC was performed. Image quality of the different sequences was assessed qualitatively and quantitatively. The findings of the same day CC served as standard of reference. RESULTS: MRC identified all polyps >5 mm (16 of 16) in size and all carcinomas (4 of 4) correctly. Fifty percent of the small polyps image quality was ranked lower in the VIBE, the image quality score of both sequences showed no statistical significant difference (chi > 0.6). CONCLUSIONS: MRC using 3D-GRE-sequences and iPAT is feasible at 3.0 T-systems. The high-resolution 3D-FLASH was slightly preferred over the 3D-VIBE because of better image quality, although both used sequences showed no statistical significant difference.

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We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization. While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.

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Exposure Fusion and other HDR techniques generate well-exposed images from a bracketed image sequence while reproducing a large dynamic range that far exceeds the dynamic range of a single exposure. Common to all these techniques is the problem that the smallest movements in the captured images generate artefacts (ghosting) that dramatically affect the quality of the final images. This limits the use of HDR and Exposure Fusion techniques because common scenes of interest are usually dynamic. We present a method that adapts Exposure Fusion, as well as standard HDR techniques, to allow for dynamic scene without introducing artefacts. Our method detects clusters of moving pixels within a bracketed exposure sequence with simple binary operations. We show that the proposed technique is able to deal with a large amount of movement in the scene and different movement configurations. The result is a ghost-free and highly detailed exposure fused image at a low computational cost.

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Skin segmentation is a challenging task due to several influences such as unknown lighting conditions, skin colored background, and camera limitations. A lot of skin segmentation approaches were proposed in the past including adaptive (in the sense of updating the skin color online) and non-adaptive approaches. In this paper, we compare three skin segmentation approaches that are promising to work well for hand tracking, which is our main motivation for this work. Hand tracking can widely be used in VR/AR e.g. navigation and object manipulation. The first skin segmentation approach is a well-known non-adaptive approach. It is based on a simple, pre-computed skin color distribution. Methods two and three adaptively estimate the skin color in each frame utilizing clustering algorithms. The second approach uses a hierarchical clustering for a simultaneous image and color space segmentation, while the third approach is a pure color space clustering, but with a more sophisticated clustering approach. For evaluation, we compared the segmentation results of the approaches against a ground truth dataset. To obtain the ground truth dataset, we labeled about 500 images captured under various conditions.