Abstract
This paper discusses the suitability of reconfigurable computing to speedup medical image classification problems. As an example of the speedup offered by reconfigurable logic, a multispectral computer vision system for automatic diagnosis of prostatic cancer is implemented. Different parallel architectures for various steps in automatic diagnosis are proposed and implemented in Field Programmable Gate Arrays (FPGAs). The first step of the algorithm is to compute Grey Level Co-occurrence Matrix (GLCM). The second step involves the normalisation of GLCM. The third step of the algorithm is to compute texture features from the normalised GLCM. The last step is concerned with image classification using linear discriminant analysis (LDA). Finally, the performance of the proposed system is assessed and compared against a microprocessor based solution. The results obtained clearly show that the proposed solution compares favorably.
Keywords
- Texture Feature
- Linear Discriminant Analysis
- Field Programmable Gate Array
- Multispectral Image
- Parallel Architecture
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Roula, M.A., et al.: A Multispectral Computer Vision System for Automatic Grading of Prostatic Neoplasia. In: IEEE International Symp. on Biomedical Imaging (2002)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Trans. on Systems, Man, and Cybernetics 3(6), 610–621 (1973)
Conners, R.W., Harlow, C.A.: A Theoretical Comaprison of Texture Algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence 2, 204–222 (1980)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2001)
Sharma, A.: Programmable Logic Handbook, PLDs, CPLDs and FPGAs. McGraw-Hill, New York (1998)
Wikantika, K., Harto, A.B., Tateishi, R.: The use of spectral and textural features from Landsat TM image for land cover classification in mountainous area. In: Proceedings of the IECL Japan workshop, Tokyo (2001)
Harman, H.H.: Modern factor analysis, 3rd edn. University of Chicago Press, Chicago (1976)
Tatsuoka, M.M.: Multivariate analysis. John Wiley and Sons, New York (1971)
Tahir, M.A., Bouridane, A., Kurugollu, F., Amira, A.: An FPGA based coprocessor for calculating grey level cooccurrence matrix. In: Proceedings of the 46th IEEE International Midwest Symposium on Circuits and Systems (2003)
Tahir, M.A., et al.: An FPGA based co-processor for GLCM texture features measurement. In: Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tahir, M.A., Bouridane, A., Kurugollu, F. (2004). An FPGA Based Coprocessor for the Classification of Tissue Patterns in Prostatic Cancer. In: Becker, J., Platzner, M., Vernalde, S. (eds) Field Programmable Logic and Application. FPL 2004. Lecture Notes in Computer Science, vol 3203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30117-2_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-30117-2_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22989-6
Online ISBN: 978-3-540-30117-2
eBook Packages: Springer Book Archive