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Medical image processing is one of the most famous image processing fields in this era. This fame comes because of the big revolution in information technology that is used to diagnose many illnesses and saves patients lives. There are many image processing techniques used in this field, such as image reconstructing, image segmentation and many more. Image segmentation is a mandatory step in many...
Effective and fast localization of anatomical structures is a crucial first step towards automated analysis of medical volumes. In this paper, we propose an iterative approach for structure localization in medical volumes based on the adaptive bandwidth mean-shift algorithm for object detection (ABMSOD). We extend and tune the ABMSOD algorithm, originally used to detect 2D objects in non-medical images,...
Time sequences of 3D images of cerebral and other vasculature blood flow during surgery and other medical procedures allow enhanced visual feedback. The visual feedback constitutes an enhancement over the existing 2D time series of X-ray projections as it facilitates the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. An algorithm that outputs...
List-mode processing is an efficient way of dealing with the sparse nature of PET data sets, and is the processing method of choice for time-of-flight (ToF) PET. We present a novel method of computing line projection operations required for list-mode ordered subsets expectation maximization (OSEM) for fully 3-D PET image reconstruction on a graphics processing unit (GPU) using the compute unified...
Accurate scatter correction is especially important for high-resolution 3D PETs due to the lack of inter-slice septa. To address this problem, a fully 3D iterative scatter-corrected OSEM in which a 3D single scatter simulation (SSS) is alternatively performed with a 3D OSEM reconstruction until convergence was recently proposed. However, due to the computational complexity of both SSS and OSEM algorithms...
The segmentation of cervical vertebra in X-Ray radiographs can give valuable information for the study of the vertebral mobility. One particular characteristic of the X-Ray images is that they present very low grey level variation and makes the segmentation difficult to perform. In this paper, we propose a segmentation procedure based on the Active Shape Model to deal with this issue. However, this...
The Compute Unified Device Architecture (CUDA) is a new programming platform making use of the unified shader design of the most current Graphics Processing Units (GPUs) from NVIDIA. In this paper, we apply this revolutionary new technology to implement the automatic time gain compensation (ATGC) for medical ultrasound imaging. The parallel box filtering method and general matrix computation algorithms...
Image registration is an indispensable process in the detection of brain structural and anatomical abnormities. Inverse-consistency, topology preserving and real time application are essential to provide accurate deformation fields for statistical analysis of brain variability. Unfortunately, the previous algorithms lacked of these features. We present a registration method by adapting the optimization...
We propose a fast implementation for iterative MR image reconstruction using Graphics Processing Units (GPU). In MRI, iterative reconstruction with conjugate gradient algorithms allows for accurate modeling the physics of the imaging system. Specifically, methods have been reported to compensate for the magnetic field inhomogeneity induced by the susceptibility differences near the air/tissue interface...
We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work...
We describe the implementation of an image reconstruction algorithm using the parallel processing capabilities of graphics processors. We are designing a new breast scanner which will allow simultaneous acquisition of PET and MRI images. The breast scanner is based on the technology of the much smaller RatCAP PET detector. The image reconstruction of the breast scanner poses a significant computing...
In order to improve the image quality and rendering speed, how to deal with a large scale of voxel computation is a challenge for programmers who work at medical image visualization. CUDA is a parallel programming model and software environment designed to overcome this challenge while maintaining a low learning curve for programmers familiar with standard programming languages such as C. In this...
We present an on-line list-mode image reconstruction system using GPUs for a surgical PET imaging probe system. We used the nVidia GeForce 9800GTX+ and CUDA to reconstruct images. The proposed system can generate a three-dimensional image from simulated data in 70 msec. We also compared the processing time with respect to the number of LORs per subset. We are working on optimizing the CUDA code to...
A CUDA implementation of the existing software FIRST (Fast Iterative Reconstruction Software for (PET) Tomography) is presented. This implementation uses consumer graphics processing units (GPUs) to accelerate the compute-intensive parts of the reconstruction: forward and backward projection. FIRST was originally developed in FORTRAN, and it has been migrated to C language to be used with NVIDIA C...
Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper,...
In this paper, we present a method that detects intracranial space-occupying lesions in two-dimensional (2D) brain high-resolution CT images. Use of statistical texture atlas technique localizes anatomy variation in the gray level distribution of brain images, and in turn, identifies the regions with lesions. The statistical texture atlas involves 147 HRCT slices of normal individuals and its construction...
Although iterative reconstruction techniques (IRTs) have been shown to produce images of superior quality over conventional filtered back projection (FBP) based algorithms, the use of IRT in a clinical setting has been hampered by the significant computational demands of these algorithms. In this paper we present results of our efforts to overcome this hurdle by exploiting the combined computational...
The biomedical imaging chain is continuously being challenged to reconstruct, analyze, and visualize increasing amounts of data in shorter amounts of time. Parallel computing on multi-core devices and clustered computers has allowed for continued innovation of compute and processing technologies but not without facing serious constraints of cost, space, and power consumption. Over the last three years...
PROPELLER technique can effectively cancel motion artifacts in MRI. But its wider application in clinical situation is limited due to considerable reconstruction times. Since most correction operations in PROPELLER reconstruction can be done for each strip respectively, the algorithm is highly parallelizable. This allows us to exploit the data-parallel nature of the GPU fragment processors. The paper...
This article presents the details about the acceleration of 2D wavelet-based medical data (image) compression on MATLAB with CUDA. It is obvious that the diagnostic materials (mostly as acertain type of image) are increasingly acquired in a digital format. Therefore, common need to daily manipulate huge amount of data brought about the issue of compression within a very less stipulated amount of time...
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