Title: Stroma classification for neuroblastoma on graphics processors
Authors: Antonio Ruiz, Olcay Sertel, Manuel Ujaldon, Umit Catalyurek, Joel Saltz, Metin N. Gurcan
Addresses: Computer Architecture Department, University of Malaga, Malaga, Spain. ' Biomedical Informatics Department, Ohio State University, Columbus, Ohio, USA. ' Computer Architecture Department, University of Malaga, Malaga, Spain. ' Biomedical Informatics Department, Ohio State University, Columbus, Ohio, USA. ' Biomedical Informatics Department, Ohio State University, Columbus, Ohio, USA. ' Biomedical Informatics Department, Ohio State University, Columbus, Ohio, USA
Abstract: Neuroblastoma is one of the most common childhood cancers. We are developing an image analysis system to assist pathologists in their prognosis. Since this system operates on relatively large-scale images and requires sophisticated algorithms, computerised analysis takes a long time to execute. In this paper, we propose a novel approach to benefit from high memory bandwidth and strong floating-point capabilities of graphics processing units. The proposed approach achieves a promising classification accuracy of 99.4% and an execution performance with a gain factor up to 45 times compared to hand-optimised C++ code running on the CPU.
Keywords: neuroblastoma; computer-aided prognosis; feature extraction; image processing; graphics processors; bioinformatics; stroma classification; childhood cancer; image analysis; classification accuracy.
DOI: 10.1504/IJDMB.2009.026702
International Journal of Data Mining and Bioinformatics, 2009 Vol.3 No.3, pp.280 - 298
Published online: 23 Jun 2009 *
Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article