Abstract
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.
Highlights
One of the most diffused types of cancer is the brain tumor, which has an estimated incidence of 3.4 per 100, 000 subjects [1]
We present the parallelization of the K-means algorithm on different parallel architectures in order to evaluate which one is more suitable for real-time processing
We presented different parallel implementations of the K-means algorithm for hyperspectral medical image clustering
Summary
One of the most diffused types of cancer is the brain tumor, which has an estimated incidence of 3.4 per 100, 000 subjects [1]. There are different types of brain tumors; the most common one concerns the glial cells of the brain and is called glioma. It accounts from the 30% to the 50% of the cases. In the 85% of these cases, it is a malignant tumor called glioblastoma. These kind of gliomas are characterized by fast-growing invasiveness, which is locally very aggressive, in most cases unicentric and rarely metastasizing [2].
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