A parallel processing model for big medical image data

Parallel computing has gained a great influence on scientific researches and in our daily life, especially when dealing with big data. One of the preconditions of high performance on computing is the support of efficient algorithms, which should be divisible and computing simultaneously. But not all algorithms are applicable for parallel computing, sometimes it can only make use of one single processor. In order to take full advantages of cluster or Multi-core CPUs in that case, A pipeline computation model is proposed which applies on cluster to make procedures more efficient and make full use of computer resources. Especially, our model has a very good performance on medical image process. With the model, almost all the positions of the organs in CT-images of a person could be found out simultaneously and accurately in one time, which can efficiently speed up the diagnosis of doctors, rather than the serial algorithm which can only find the position of one organ in one time before. The result of our experiment shows that the performance of the former serial algorithm has been improved by 40 percent by using our method.

[1]  Dhabaleswar K. Panda,et al.  Optimizing MPI Communication on Multi-GPU Systems Using CUDA Inter-Process Communication , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[2]  Alan Wagner,et al.  Added Concurrency to Improve MPI Performance on Multicore , 2012, 2012 41st International Conference on Parallel Processing.

[3]  Chengwen Chu,et al.  Multi-organ segmentation from 3D abdominal CT images using patient-specific weighted-probabilistic atlas , 2013, Medical Imaging.

[4]  Wang,et al.  Improved Strategies for Parallel Medical Image Processing Applications , 2008 .

[5]  Min Tang,et al.  Parallel visualization of large medical datasets based on computer cluster , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[6]  Piyamas Suapang,et al.  Web-based Medical Image Archiving and Communication System for Teleimaging , 2011, 2011 11th International Conference on Control, Automation and Systems.

[7]  Phuong Nguyen,et al.  Terabyte-sized image computations on Hadoop cluster platforms , 2013, 2013 IEEE International Conference on Big Data.

[8]  Jianqing Zhang,et al.  The Parallel Computing Based on Cluster Computer in the Processing of Mass Aerial Digital Images , 2008, 2008 International Symposiums on Information Processing.

[9]  Jong-Won Park,et al.  Organ segmentation by comparing of gray value portion on abdominal CT image , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[10]  Hans-Peter Meinzer,et al.  Medical image processing and visualization on heterogenous clusters of symmetric multiprocessors using MPI and POSIX threads , 1998, Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing.

[11]  Jacob D. Furst,et al.  Automatic Single-Organ Segmentation in Computed Tomography Images , 2006, Sixth International Conference on Data Mining (ICDM'06).

[12]  Hong Wang,et al.  Fast organ segmentation in CT images with CUDA , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[13]  Tutut Herawan,et al.  Computational and mathematical methods in medicine. , 2006, Computational and mathematical methods in medicine.

[14]  Yongwang Zhao,et al.  Web-based remote collaboration over medical image using web services , 2009, 2009 Global Information Infrastructure Symposium.

[15]  Chao Hui,et al.  Parallel Processing Imaging Algorithm for Synthetic Aperture Radar based on pipeline , 2012, ITCS 2012.

[16]  Juan Chen,et al.  Network Energy Optimization for MPI Operations , 2012, 2012 Fifth International Conference on Intelligent Computation Technology and Automation.

[17]  Yen-Wei Chen,et al.  Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning , 2013, Comput. Math. Methods Medicine.