Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation

Anguo, 董安国 Dong; Jiaxun, 李佳逊 Li; Bei, 张蓓 Zhang; Miaomiao, 梁苗苗 Liang

doi:10.3788/aos201737.0828005

Cited by 4 publications

(2 citation statements)

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“…Hyperspectral imaging technology is expected to be an effective way to solve the above-mentioned problems. Spectral imaging originated in the field of traditional aerial remote sensing, and it is now crossing the broad-band and high-resolution hyperspectral imaging technology [ 10 , 11 , 12 ]. In recent years, breakthroughs in imaging methods and key parameters such as spectral resolution have made it possible for medical spectral detection at a close range [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

Tao

Xue

et al. 2023

Diagnostics

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In order to improve the clinical application of hyperspectral technology in the pathological diagnosis of tumor tissue, a joint diagnostic method based on spectral-spatial transfer features was established by simulating the actual clinical diagnosis process and combining micro-hyperspectral imaging with large-scale pathological data. In view of the limited sample volume of medical hyperspectral data, a multi-data transfer model pre-trained on conventional pathology datasets was applied to the classification task of micro-hyperspectral images, to explore the differences in spectral-spatial transfer features in the wavelength of 410–900 nm between tumor tissues and normal tissues. The experimental results show that the spectral-spatial transfer convolutional neural network (SST-CNN) achieved a classification accuracy of 95.46% for the gastric cancer dataset and 95.89% for the thyroid cancer dataset, thus outperforming models trained on single conventional digital pathology and single hyperspectral data. The joint diagnostic method established based on SST-CNN can complete the interpretation of a section of data in 3 min, thus providing a new technical solution for the rapid diagnosis of pathology. This study also explored problems involving the correlation between tumor tissues and typical spectral-spatial features, as well as the efficient transformation of conventional pathological and transfer spectral-spatial features, which solidified the theoretical research on hyperspectral pathological diagnosis.

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Section: Introductionmentioning

confidence: 99%

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

Tao

Xue

et al. 2023

Diagnostics

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“…While medical microspectral imaging is an integrated crossover technology based on many disciplines such as clinical medicine, imaging, and pathological analysis. Spectral analysis can obtain a complete spectrum of a certain point on the biological tissue sample in the wavelength range of interest [6], and thereby analyze the chemical composition and physical characteristic of different pathological tissues; optical imaging technology provides spatial distribution information of each micro structure to achieve a visual representation of different pathological structures. Medical micro-hyperspectra combines two-dimensional image information with one-dimensional spectral signal into a three-dimensional datacube, which not only includes rich spatial information, but also contains spectral information that reflects biological tissue characteristics.…”

Section: Introductionmentioning

confidence: 99%

Tumor tissue classification based on micro-hyperspectral technology and deep learning

Zhang

et al. 2019

Biomed. Opt. Express

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In order to explore the application of hyperspectral technology in the pathological diagnosis of tumor tissue, we used microscopic hyperspectral imaging technology to establish a hyperspectral database of 30 patients with gastric cancer. Based on the difference in spectralspatial features between gastric cancer tissue and normal tissue in the wavelength of 410-910 nm, we propose a deep-learning model-based analysis method for gastric cancer tissue. The microscopic hyperspectral feature and individual difference of gastric tissue, spatial-spectral joint feature and medical contact are studied. The experimental results show that the classification accuracy of proposed model for cancerous and normal gastric tissue is 97.57%, the sensitivity and specificity of gastric cancer tissue are 97.19% and 97.96% respectively. Compared with the shallow learning method, CNN can fully extract the deep spectral-spatial features of tumor tissue. The combination of deep learning model and micro-spectral analysis provides new ideas for the research of medical pathology.

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Multidimensional relation learning for hyperspectral image classification

Fang

Cao

2020

Neurocomputing

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Hyperspectral Image Classification Algorithm Based on Spectral Clustering and Sparse Representation

Cited by 4 publications

References 0 publications

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

Tumor tissue classification based on micro-hyperspectral technology and deep learning

Multidimensional relation learning for hyperspectral image classification

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