An FPGA Based Coprocessor for GLCM and Haralick Texture Features and their Application in Prostate Cancer Classification

Authors:
M. A. Tahir;A. Bouridane;F. Kurugollu
Affiliations:
School of Computer Science, Queen's University Belfast, Belfast, UK BT7 1NN;School of Computer Science, Queen's University Belfast, Belfast, UK BT7 1NN;School of Computer Science, Queen's University Belfast, Belfast, UK BT7 1NN
Venue:
Analog Integrated Circuits and Signal Processing
Year:
2005

Citing 1
Cited 9

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ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
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ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Parallel implementation of Gray Level Co-occurrence Matrices and Haralick texture features on cell architecture

The Journal of Supercomputing
Prostate cancer region prediction by fusing results from MALDI spectra-processing and texture analysis

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High performance implementation of texture features extraction algorithms using FPGA architecture

Journal of Real-Time Image Processing

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Abstract

Grey Level Co-occurrence Matrix (GLCM), one of the best known tool for texture analysis, estimates image properties related to second-order statistics. These image properties commonly known as Haralick texture features can be used for image classification, image segmentation, and remote sensing applications. However, their computations are highly intensive especially for very large images such as medical ones. Therefore, methods to accelerate their computations are highly desired. This paper proposes the use of programmable hardware to accelerate the calculation of GLCM and Haralick texture features. Further, as an example of the speedup offered by programmable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer has been implemented. The performance is then compared against a microprocessor based solution.