A Parallel Implementation of 4-Dimensional Haralick Texture Analysis for Disk-Resident Image Datasets

Woods, Brent; Clymer, Bradley D.; Saltz, Joel; Kurç, Tahsin

doi:10.1109/sc.2004.5

Cited by 6 publications

(7 citation statements)

References 23 publications

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“…In our experience with the filter-stream programming model, most applications are bottleneck free, and the number of active internal tasks are higher than the available processors [8,11,17,35,40,43]. Thus, the proposed approach to exploit heterogeneous resources consists of allocating multiple tasks concurrently to processors where they will perform the best, as detailed in Sect.…”

Section: Motivating Applicationmentioning

confidence: 99%

Optimizing dataflow applications on heterogeneous environments

et al. 2011

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The increases in multi-core processor parallelism and in the flexibility of many-core accelerator processors, such as GPUs, have turned traditional SMP systems into hierarchical, heterogeneous computing environments. Fully exploiting these improvements in parallel system design remains an open problem. Moreover, most of the current tools for the development of parallel applications for hierarchical systems concentrate on the use of only a single processor type (e.g., accelerators) and do not coordinate several heterogeneous processors. Here, we show that making use of all of the heterogeneous computing resources can significantly improve application performance. Our approach, which consists of optimizing applications at run-time by efficiently coordinating application task execution on all available processing units is evaluated in the context of replicated dataflow applications. The proposed techniques were developed and implemented in an integrated run-time system targeting both intra-and inter-node parallelism. The experimental results with a real-world complex biomedical application show that our approach nearly doubles the performance of the GPU-only implementation on a distributed heterogeneous accelerator cluster.

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Section: Motivating Applicationmentioning

confidence: 99%

Optimizing dataflow applications on heterogeneous environments

et al. 2011

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“…Finally, the window is moved in the t-dimension from precontrast to the final acquisition in the temporal sequence. Although texture analysis is a computationally intensive process, it can be performed quickly and efficiently with the use of parallel computing (20).…”

Section: Texture Analysismentioning

confidence: 99%

Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data

Woods

Clymer

Kurç

et al. 2007

Magnetic Resonance Imaging

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Purpose:To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods:4D texture analysis was performed on DCE-MRI data sets of breast lesions. A modelfree neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results:The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with ␣ ϭ 0.05. The results show that an area under the ROC curve (A z ) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion:This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted.

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“…When the volume of data that needs to be processed is in the order of terabytes, the computational burden in generating texture feature vectors becomes a major stumbling block. Recently there has been some work on developing efficient computing methods for accelerating GLCM texture feature computation (Clausi and Yongping 2002;Tahir et al 2004;Woods et al 2004). Clausi et al (2002) reported significant speed-up using special data structures called linked-lists and hash tables to compute GLCM texture features.…”

Section: Related Workmentioning

confidence: 99%

Mapping of settlements in high-resolution satellite imagery using high performance computing

et al. 2007

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Classifying urban land cover from highresolution satellite imagery is challenging, and those challenges are compounded when the imagery databases are very large. Accurate land cover data is a crucial component of the population distribution modeling efforts of the Oak Ridge National Laboratory's (ORNL) LandScan Program. Currently, LandScan Program imagery analysts manually interpret high-resolution (1-5 m) imagery to augment existing satellite-derived medium (30 m) and coarse (1 km) resolution land cover datasets. For LandScan, the high-resolution image archives that require interpretation are on the order of terabytes. The goal of this research is to automate human settlement mapping by utilizing ORNL's high performance computing capabilities. Our algorithm employs gray-level and local edge-pattern co-occurrence matrices to generate texture and edge patterns. Areas of urban land cover correlate with statistical features derived from these texture and edge patterns. We have parallelized our algorithms for implementation on a 64-node system using a single instruction multiple data programming model (SIMD) with Message Passing Interface (MPI) as the communication mode. Our parallel-configured classifier performs 30-40 times faster than stand-alone alternatives. We have tested our system on IKONOS imagery. The early results are promising, pointing towards future large-scale classification of human settlements at high-resolution.

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A Parallel Implementation of 4-Dimensional Haralick Texture Analysis for Disk-Resident Image Datasets

Abstract: Abstract

Cited by 6 publications

References 23 publications

Optimizing dataflow applications on heterogeneous environments

Optimizing dataflow applications on heterogeneous environments

Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data

Mapping of settlements in high-resolution satellite imagery using high performance computing

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