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

Authors: Tahir, M.¹; Bouridane, A.²; Kurugollu, F.²

Source: Analog Integrated Circuits and Signal Processing, Volume 43, Number 2, May 2005 , pp. 205-215(11)

Publisher: Springer

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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.

Keywords: FPGAs; Multispectral images; Medical image classification; GLCM; Haralick texture features

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s10470-005-6793-2

Affiliations: 1: School of Computer Science, Queen’s University Belfast, Belfast, BT7 1NN, UK, Email: a.tahir@qub.ac.uk 2: School of Computer Science, Queen’s University Belfast, Belfast, BT7 1NN, UK,

Publication date: 2005-05-01