Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boun...
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ISBN:
(纸本)9781510625501
Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts' manual reference segmentations. The measured mean standard deviation of error values for the whole gland were 1.4 +/- 0.4 mm, 8.5 +/- 2.0 mm, and 86 +/- 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5 mm (MAD), 9.0 mm (HDist), and 83% (DSC). The proposed algorithm illustrated a fast, accurate, and robust performance for 3D prostate segmentation. The accuracy of the algorithm is within the inter-expert variability observed in manual segmentation and comparable to the best performance results reported in the literature.
More and more imageprocessing applications are intended to run on mobile devices. This trend is visible also in medical industry, which have very strict reliability and safety requirements. Increased power consumptio...
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ISBN:
(纸本)9781538666289
More and more imageprocessing applications are intended to run on mobile devices. This trend is visible also in medical industry, which have very strict reliability and safety requirements. Increased power consumption and inefficient imageprocessing can affect both of these factors significantly. More over, it affects the user experience as well. In this paper authors investigates the performance and power consumption of several different numeric algorithms implemented on iPhone device. The comparison is made between CPU and GPU based solutions. Authors evaluated the test scenarios involving common used operations: matrix multiplication, thresholding and Canny's edge detection. The results showed that GPU based solution can be up to 4 times faster as CPU based one, with similar power footprint in particular cases. All measurements have been gathered without hardware interference nor jailbreaking of the device. Finally authors discuss possible application considerations and scenarios in medical related imageprocessing smartphone apps.
image segmentation is one of the most significant and inevitable task in variety areas ranging from face/object/character recognition and medical imaging applications to robotic control and self-driving vehicular syst...
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ISBN:
(纸本)9781538676417
image segmentation is one of the most significant and inevitable task in variety areas ranging from face/object/character recognition and medical imaging applications to robotic control and self-driving vehicular systems. Accuracy and processing time of image segmentation processes are also prominent parameters for quality of such computer vision systems. The proposed method incorporates three main pre-processing techniques such as Down Scaling/Sampling, Gamma Correction and Edge Preserving Smoothing so as to achieve accuracy and robustness of the segmentation. Pre-processing techniques are performed for both Fuzzy C-means (FCM) and K-means algorithm and all RGB information of image are taken into consideration while segmenting the image rather than using only gray scale. Performance analysis are performed on real-world images. Experiments show that, our method achieve higher accuracy levels and feasible processing time results compared to conventional FCM and K-means algorithms.
Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows real-time applications on low-powered d...
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ISBN:
(纸本)9781479970612
Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows real-time applications on low-powered devices. The key idea is to learn a set of kernels that upscales, filters, and blends patches of different scales guided by local structure analysis. This approach is trainable so that learned filters are capable of treating diverse noise patterns and artifacts. Experimental results show that the presented approach produces comparable results to state-of-the-art algorithms while processing time is orders of magnitude faster.
A rigid registration is a crucial initial step for a correct deformable medical image registration. In this work, we propose rigid registration method resistant to large deformations and missing data. The proposed met...
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ISBN:
(纸本)9781538669792
A rigid registration is a crucial initial step for a correct deformable medical image registration. In this work, we propose rigid registration method resistant to large deformations and missing data. The proposed method is based on the bones segmentation, feature matching and outliers elimination inspired by traditional computer vision approach. The method is compared to other state-of-the- art algorithms, the iterative closest point and intensity-based registration using widely available dataset. The proposed algorithm does not fail into local minima and reconstructs correct deformations for average vector length greater than 150 mm and data overlap ratio less than 50%, where currently applied methods fail. The algorithm is evaluated using angle and magnitude errors between corresponding deformation vectors, Hausdorff distance between bone segmentations and resistance to fail into local minima.
Many industrial machine vision problems, particularly real-time control of manufacturing processes such as laser cladding, require robust and fast imageprocessing. The inherent disturbances in images acquired during ...
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Many industrial machine vision problems, particularly real-time control of manufacturing processes such as laser cladding, require robust and fast imageprocessing. The inherent disturbances in images acquired during these processes makes classical segmentation algorithms uncertain. Among many convolutional neural networks introduced recently to solve such difficult problems, U-Net balances simplicity with segmentation accuracy. However, it is too computationally intensive for usage in many real-time processing *** this work we present a method of identifying the most informative levels of detail in the U-Net. By only processing the image at the selected levels, we reduce the total computation time by 80%, while still preserving adequate quality of segmentation.
In this paper, hardware implementation of corner detection at real time video signals using Harris filter based on FPGA is explained. Corner detection is an elemantary and fundamental tool for image segmentation and f...
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ISBN:
(纸本)9781538615010
In this paper, hardware implementation of corner detection at real time video signals using Harris filter based on FPGA is explained. Corner detection is an elemantary and fundamental tool for image segmentation and feature extraction like edge detection. Very high speed hardware like FPGA's are used to implement the image and video processingalgorithms for improving the performance of processingsystems. algorithms are implemented on the Xilinx Zynq 7000. The video input signals come from a laptop's HDMI interface to FPGA in order to filter and the detected corners are displayed on a HDMI display screen.
Modern neural network-based algorithms are able to produce highly accurate depth estimates from stereo image pairs, nearly matching the reliability of measurements from more expensive depth sensors. However, this accu...
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ISBN:
(数字)9781728165530
ISBN:
(纸本)9781728165547
Modern neural network-based algorithms are able to produce highly accurate depth estimates from stereo image pairs, nearly matching the reliability of measurements from more expensive depth sensors. However, this accuracy comes with a higher computational cost since these methods use network architectures designed to compute and process matching scores across all candidate matches at all locations, with floating point computations repeated across a match volume with dimensions corresponding to both space and disparity. This leads to longer running times to process each image pair, making them impractical for real-time use in robots and autonomous vehicles. We propose a new stereo algorithm that employs a significantly more efficient network architecture. Our method builds an initial match cost volume using traditional matching costs that are fast to compute, and trains a network to estimate disparity from this volume. Crucially, our network only employs per-pixel and two-dimensional convolution operations: to summarize the local match information at each location as a lowdimensional feature vector, and to spatially process these "cost-signature" features to produce a dense disparity map. Experimental results on KITTI show that our method delivers competitive accuracy at significantly higher speeds- running at 48 frames per second on a modern GPU.
One algorithin for thresholding using a histogram median is presented in this paper. The algorithm is named HisMedian and it is implemented on Java. It is also proposed a taxonomy of thresholding algorithms based on a...
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ISBN:
(纸本)9781450364256
One algorithin for thresholding using a histogram median is presented in this paper. The algorithm is named HisMedian and it is implemented on Java. It is also proposed a taxonomy of thresholding algorithms based on a method for defining threshold value as a real color of image or calculated color's value. According to the proposed taxonomy a set of popular thresholding algorithms (including HisMedian) is experimentally evaluated using three test images. The experimental results show that if a histogram is bimodal then the algorithms which use real color(s) from the image as a threshold(s) achieve better results than algorithms which use calculated value(s) as a threshold(s).
Most of the applications in mobile robots use the image data from the monitoring system to compile algorithms. The undesired movement in the video is an obstacle to compute commands, and it requires to reduce this mot...
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