Accelerating the K-Nearest Neighbors Filtering Algorithm to Optimize the Real-Time Classification of Human Brain Tumor in Hyperspectral Images

Sensors
Giordana FlorimbiRoberto Sarmiento

Abstract

The use of hyperspectral imaging (HSI) in the medical field is an emerging approach to assist physicians in diagnostic or surgical guidance tasks. However, HSI data processing involves very high computational requirements due to the huge amount of information captured by the sensors. One of the stages with higher computational load is the K-Nearest Neighbors (KNN) filtering algorithm. The main goal of this study is to optimize and parallelize the KNN algorithm by exploiting the GPU technology to obtain real-time processing during brain cancer surgical procedures. This parallel version of the KNN performs the neighbor filtering of a classification map (obtained from a supervised classifier), evaluating the different classes simultaneously. The undertaken optimizations and the computational capabilities of the GPU device throw a speedup up to 66.18× when compared to a sequential implementation.

References

Apr 22, 2003·IEEE Transactions on Medical Imaging·Chuin-Mu WangChein-I Chang
Nov 30, 2004·IEEE Engineering in Medicine and Biology Magazine : the Quarterly Magazine of the Engineering in Medicine & Biology Society·Tuan Vo-DinhXiaogang Pan
Mar 22, 2007·Cancer Biology & Therapy·Svetlana V PanasyukAdrianne E Rogers
Jan 21, 2014·Journal of Biomedical Optics·Guolan Lu, Baowei Fei
Apr 17, 2014·International Journal of Computer Assisted Radiology and Surgery·Megumu MoriMakoto Hashizume

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Methods Mentioned

BETA
imaging technique
PCA

Software Mentioned

WSize14
CUBLAS
Kepler
WSize6
WSize8
WSize2
Release
CUDA

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