Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.
Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.
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DOI：
10.1007/s11554-016-0611-1
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发表时间：
2017-03
影响因子：
3
通讯作者：
Dong X
中科院分区：
文献类型：
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作者：
Shirvaikar M;Lagadapati Y;Dong X
骨质疏松性骨折是老年人和女性人群医疗保健的主要问题。通过使用变异函数分析等技术对骨图像数据进行二阶统计分析，可以增强对具有高风险骨质疏松性骨折的患者的早期诊断。这种分析是计算密集型的，从而对引入常见临床环境中的成像机器造成了障碍。本文研究了快速实现的半变异函数算法，这已被证明是有效的建模骨强度，并应感兴趣的读者在计算机辅助诊断和定量图像分析领域。半变异函数是数据空间分布的统计度量，基于马尔可夫随机场（MRF）。半变异函数分析是一种计算密集型算法，在地球科学和遥感领域有着广泛的应用。最近，在医学成像领域的应用已经调查，导致需要有效的真实的时间的算法的实施。半方差γ（h）定义为滞后距离为h的任意两个数据位置之间的像素值的预期平方差的一半。由于需要检查正在处理的图像或子图像中的每一对像素，具有n个像素的图像窗口的基本算法复杂度为O（n2）。现场可编程门阵列（FPGA）由于其并行处理能力而对于这种要求苛刻的应用是有吸引力的解决方案。FPGA还倾向于以相对适中的时钟速率（以几百兆赫为单位测量）工作。本文提出了一种使用两个定制的FPGA架构的半变异函数的快速计算技术。选择模块化架构方法以允许处理单元的复制。由于像素对的并发处理，这允许高吞吐量。当前实现仅关注各向同性半变异函数计算。该算法使用VHDL在Xilinx XUPV 5-LX 110 T开发套件上进行基准测试，该开发套件采用Virtex 5 FPGA。DXA扫描的医学图像数据用于实验。实施结果表明，在计算速度方面的显着优势是实现在个人计算机上与英特尔i7多核处理器的架构。
Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O (n2) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor.