A suite of parallel algorithms for efficient band selection from hyperspectral images

Fontanella, Alessandro; Marenzi, Elisa; Torti, Emanuele; Danese, Giovanni; Leporati, Francesco

doi:10.1007/s11554-018-0765-0

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The seventh paper entitled "A suite of parallel algorithms for efficient band selection from hyperspectral images" by Fontanella et al [7] presents a suite of parallel algorithms for efficient band selection from hyperspectral images. Specifically, to have an optimized C serial version of each algorithm, the OpenMP and CUDA versions have been derived respectively for multi-core CPU and multi-core graphics processing unit (GPU).…”

Section: Real-time Image Processingmentioning

confidence: 99%

Special issue on advances in real-time image processing for remote sensing

Chen

Gao

et al. 2018

J Real-Time Image Proc

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Section: Real-time Image Processingmentioning

confidence: 99%

Special issue on advances in real-time image processing for remote sensing

Chen

Gao

et al. 2018

J Real-Time Image Proc

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“…Despite this, in our work we choose to exploit GPUs since they assure higher performance. Moreover, GPUs are going to be increasingly used for real-time image processing [13][14][15], together with other scientific applications related to simulation and modeling or machine learning in biomedical applications [16,17].…”

Section: Introductionmentioning

confidence: 99%

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

et al. 2018

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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.

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Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging

La Salvia,

Torti,

Marenzi

et al. 2024

J Supercomput

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Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments’ development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.

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A suite of parallel algorithms for efficient band selection from hyperspectral images

Cited by 9 publications

References 21 publications

Special issue on advances in real-time image processing for remote sensing

Special issue on advances in real-time image processing for remote sensing

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging

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