Publication (Journal publications)

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

Juárez Martínez, Eduardo; Sanz Álvaro, César; Salvador, Rubén; Lazcano López, Raquel; Madroñal Quintín, Daniel; Florimbi, Giordana; Fabelo, Himar; Torti, Emanuele; Ortega, Samuel; Leporati, Francesco; Danese, Giovanni; Báez-Quevedo, Abelardo; Callicó, Gustavo M.; Sarmiento, Roberto

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.

Research areas:

• Image and Video Technologies

Year:

2018

Type of Publication:

Journal publications

Keywords:

K-nearest neighbors filtering_hyperspectral imaging instrumentation_brain cancer detection_image processing_graphics processing units

Journal:

Sensors

Volume:

18

Number:

2314

Pages:

1-20

Month:

July

DOI:

10.3390/s18072314