Accelerating 3D medical volume segmentation using GPUs

Multimedia Tools and Applications - Medical images have an undeniably integral role in the process of diagnosing and treating of a very large number of ailments. Processing such images (for...     

Accelerating the Lyapack library using GPUs

Lyapack is a package for the solution of large-scale sparse problems arising in control theory. The package has a modular design, and is implemented as a Matlab toolbox, which renders it easy to utilize, modify and extend with new functionality. However, in general, the use of Matlab in combination with a general-purpose multi-core architecture (CPU) offers limited performance when tackling the sparse linear algebra operations underlying the numerical methods involved in control theory. In this paper we extend Lyapack to leverage the computational power of graphics processors (GPUs). The experimental evaluation of a new CUDA-enabled solver for the Lyapunov equation, a crucial operation appearing in control theory problems, shows a significant runtime reduction when compared with the original CPU version of Lyapack, while retaining the usability of a Matlab-based implementation.     

Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations

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 image processing based diagnosis procedures. Many segmentation algorithms use clustering approach. In this paper, we focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. In many cases, these algorithms need long execution times. In this paper, we accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. We achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy.     

Parallel watershed transformation algorithms for image segmentation

The watershed transformation is a mid-level operation used in morphological image segmentation. Techniques applied on large images, which must often complete fast, are usually computationally expensive and complex entailing efficient parallel algorithms. Two distributed approaches of the watershed transformation are introduced in this paper. The algorithms survey in a Single Program Multiple Data (SPMD) model both local and global connectivity properties of the morphological gradient of a gray-scale image to label connected components. The sequentiality of the serial algorithm is broken in the parallel versions by exploiting the ordering relation between two neighboring pixels successively incorporated in the same region. Thus, a path is traced, for every unlabeled pixel, down to its region of inclusion (whose label is then propagated backwards); in the second algorithm, regions grow independently around their seeds. In both cases only pixels which satisfy the ordering relation are incorporated in any region. This way, not only different regions are explored in a parallel fashion, but also different parts of the same region, when the latter extends to neighboring subdomains, are treated likewise. Running time and relative speedup evaluated on a Cray T3D parallel computer are used to appreciate the performance of both algorithms.     

A generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms

Segmentation of an image into regions and the labeling of the regions is a challenging problem. In this paper, an approach that is applicable to any set of multifeature images of the same location is derived. Our approach applies to, for example, medical images of a region of the body; repeated camera images of the same area; and satellite images of a region. The segmentation and labeling approach described here uses a set of training images and domain knowledge to produce an image segmentation system that can be used without change on images of the same region collected over time. How to obtain training images, integrate domain knowledge, and utilize learning to segment and label images of the same region taken under any condition for which a training image exists is detailed. It is shown that clustering in conjunction with image processing techniques utilizing an iterative approach can effectively identify objects of interest in images. The segmentation and labeling approach described here is applied to color camera images and two other image domains are used to illustrate the applicability of the approach.     

Accelerating Advanced MRI Reconstructions on GPUs

Computational acceleration on graphics processing units (GPUs) can make advanced magnetic resonance imaging (MRI) reconstruction algorithms attractive in clinical settings, thereby improving the quality of MR images across a broad spectrum of applications. This paper describes the acceleration of such an algorithm on NVIDIA's Quadro FX 5600. The reconstruction of a 3D image with 128(3) voxels achieves up to 180 GFLOPS and requires just over one minute on the Quadro, while reconstruction on a quad-core CPU is twenty-one times slower. Furthermore, relative to the true image, the error exhibited by the advanced reconstruction is only 12%, while conventional reconstruction techniques incur error of 42%.     

Evolutionary segmentation of texture image using genetic algorithms towards automatic decision of optimum number of segmentation areas

Segmentation of an image composed of different kinds of texture fields has difficulty in an exact discrimination of the texture fields and a decision of the optimum number of segmentation areas in an image when the image contains similar and/or unstationary texture fields. In this paper we formulate the segmentation problem upon such images as an optimization problem and adopt evolutionary strategy of genetic algorithms for the clustering of small regions in a feature space. The purpose of this paper is to demonstrate the efficiency of genetic algorithms to the texture segmentation and to develop the automatic texture segmentation method.     

An experimental comparison of range image segmentation algorithms

A methodology for evaluating range image segmentation algorithms is proposed. This methodology involves (1) a common set of 40 laser range finder images and 40 structured light scanner images that have manually specified ground truth and (2) a set of defined performance metrics for instances of correctly segmented, missed, and noise regions, over- and under-segmentation, and accuracy of the recovered geometry. A tool is used to objectively compare a machine generated segmentation against the specified ground truth. Four research groups have contributed to evaluate their own algorithm for segmenting a range image into planar patches     

Accelerating dissipative particle dynamics with multiple GPUs

Dissipative particle dynamics (DPD) simulation is implemented on multiple GPUs by using NVIDIA’s Compute Unified Device Architecture (CUDA) in this paper. Data communication between each GPU is executed based on the POSIX thread. Compared with the single-GPU implementation, this implementation can provide faster computation speed and more storage space to perform simulations on a significant larger system. In benchmark, the performance of GPUs is compared with that of Material Studio running on a single CPU core. We can achieve more than 90x speedup by using three C2050 GPUs to perform simulations on an 80∗80∗80 system. This implementation is applied to the study on the dispersancy of lubricant succinimide dispersants. A series of simulations are performed on lubricant–soot–dispersant systems to study the impact factors including concentration and interaction with lubricant on the dispersancy, and the simulation results are agreed with the study in our present work.     

Tuner: principled parameter finding for image segmentation algorithms using visual response surface exploration

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.     

基于遗传算法的图像分割处理技术研究

旨在使用遗传算法对带有底部噪声的图像进行处理,并通过对遗传算法的改进实现处理效果的提升。结合图像分割阐述了遗传算法的工作机制,分析了适应度计算、选择、交叉和变异等主要模块的设计方法,阐明了代沟与优秀个体的关系、不同代间的个体替换关系、交叉点的选取方法与变异位置的选择、种群数量的保持等关键性问题,并给出了参数设置的具体值。使用该算法对带底部噪声的图片进行了处理,结果表明传统遗传算法可以将目标图像从存在噪声的背景中分离出来,但处理时间为7.416 s。为提高处理效率,利用进化代数和个体的适应度值自适应地调整种群的交叉概率和变异概率对传统算法进行了改进。使用改进的遗传算法对同一噪声图像进行了分割处理,结果表明改进后的遗传算法图像分割效果更佳,处理时间仅为0.751 s,效率提高了近10倍。     

基于改进遗传算法的车牌图像分割

为了解决简单遗传算法存在的早熟、收敛到全局最优解难问题,提出了适应度标定公式,提高选择压力;定义相似度概念,保留相似性差的个体,剔除相似性个体.在不增加群体规模的前提下,增加了群体的多样性.改进遗传算法能够避免简单遗传算法所存在的问题.为了解决车牌图像识别率低问题,提出基于改进遗传算法的车牌图像分割方法,进行车牌图像识别.实验证明利用此方法进行车牌图像识别效果较好.     

混合智能算法在彩色图像分割中的应用研究

对颜色空间做了全面地介绍,对当前传统的彩色图像分割技术进行了较为系统的论述,通过比较发现传统的单一分割算法在彩色图像分割中都不可避免地存在一定的不足与缺陷.而以神经网络、遗传算法和蚁群算法等智能算法进行图像分割时,由于从不同侧面反映了人类视觉感知的智能性、并行性,取得了较好的效果,推动了图像分割向智能化方向发展,但其在理论和实践上都没有达到让人满意的程度.因此,可以根据实际情况组合不同的算法,分层次地分割图像,寻找符合人类视觉感知特性的有效的彩色图像分割混合智能算法,从而弥补单一算法对彩色图像分割的不足.     

An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization

Numerous segmentation algorithms have been developed, many of them highly specific and only applicable to a reduced class of problems and image data. Without an additional source of knowledge, automatic image segmentation based on low level image features seemed unlikely to succeed in extracting semantic objects in generic images. A new region-merging segmentation technique has recently been developed which incorporates the spectral and textural properties of the objects to be detected and also their different size and behaviour at different stages of scale, respectively. Linking this technique with the FAO Land Cover Land Use classification system resulted in the development of an automated, standardized classification methodology. Testing on Landsat and Aster images resulted in mutually exclusive classes with clear and unambiguous class definitions. The error matrix based on field samples showed overall accuracy values of 92% for Aster image and 89% for Landsat. The KIA values were 88% for Aster images and 84% for the Landsat image.     

A comparison of nature inspired algorithms for multi-threshold image segmentation

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.     

Multispectral image compression using eigenregion-based segmentation

In the study, a novel segmentation technique is proposed for multispectral satellite image compression. A segmentation decision rule composed of the principal eigenvectors of the image correlation matrix is derived to determine the similarity of image characteristics of two image blocks. Based on the decision rule, we develop an eigenregion-based segmentation technique. The proposed segmentation technique can divide the original image into some proper eigenregions according to their local terrain characteristics. To achieve better compression efficiency, each eigenregion image is then compressed by an efficient compression algorithm eigenregion-based eigensubspace transform (ER-EST). The ER-EST contains 1D eigensubspace transform (EST) and 2D-DCT to decorrelate the data in spectral and spatial domains. Before performing EST, the dimension of transformation matrix of EST is estimated by an information criterion. In this way, the eigenregion image may be approximated by a lower-dimensional components in the eigensubspace. Simulation tests performed on SPOT and Landsat TM images have demonstrated that the proposed compression scheme is suitable for multispectral satellite image.     

分水岭变换在岩屑图像分割中的应用

分水岭变换是一种基于区域和数学形态学的图像分割方法,被广泛用于灰度图像的分割之中.但传统分水岭变换过分割问题严重,图像的噪声和虚假纹理会淹没真正想得到的边缘信息.针对岩屑图像的特征,提出了一种改进的分水岭算法分割方案.先在预处理期用形态学开闭重建运算对原始图像平滑处理,在相对保留边缘不受影响的同时,降低噪声的影响.再通过非线性的阈值变换分离出目标和背景,然后在提取出目标的情况下合并过小区域,得到目标的边缘.而由于阈值变换后,区域数量已经明显减少,可以降低区域合并的运算量,提高合并速度.在求取形态学梯度时,选用了一种新的形态梯度形式,消除了形态学处理对分割结果造成的轮廓偏移现象.从实验结果看来,该算法取得了较好的分割效果.     

Color image segmentation using competitive learning

Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results