scholarly journals In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39098-39116 ◽  
Author(s):  
Himar Fabelo ◽  
Samuel Ortega ◽  
Adam Szolna ◽  
Diederik Bulters ◽  
Juan F. Pineiro ◽  
...  
2015 ◽  
Vol 7 (292) ◽  
pp. 292ra100-292ra100 ◽  
Author(s):  
Carmen Kut ◽  
Kaisorn L. Chaichana ◽  
Jiefeng Xi ◽  
Shaan M. Raza ◽  
Xiaobu Ye ◽  
...  

2005 ◽  
Author(s):  
Manjeet Rege ◽  
Ming Dong ◽  
Farshad Fotouhi ◽  
Mohammad-Reza Siadat ◽  
Lucia Zamorano

Author(s):  
HIROSHI FUKUDA ◽  
SHIGEO KINOMURA ◽  
YASUYUKI TAKI ◽  
RYOI GOTO ◽  
KENTARO INOUE ◽  
...  

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 283 ◽  
Author(s):  
Emanuele Torti ◽  
Giordana Florimbi ◽  
Francesca Castelli ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
...  

The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.


1994 ◽  
Vol 31 (2) ◽  
pp. 185
Author(s):  
Yong Whee Bahk ◽  
Kyung Sub Shinn ◽  
Tae Suk Suh ◽  
Bo Young Choe ◽  
Kyo Ho Choi

2017 ◽  
Vol 30 (9) ◽  
pp. e3734 ◽  
Author(s):  
Uran Ferizi ◽  
Benoit Scherrer ◽  
Torben Schneider ◽  
Mohammad Alipoor ◽  
Odin Eufracio ◽  
...  

Author(s):  
Y Liu ◽  
D Gebrezgiabhier ◽  
J Arturo Larco ◽  
S Madhani ◽  
A Shahid ◽  
...  

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