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利用高光谱成像和无监督分类方法对正常和坏死小肠组织进行区分。

Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification.

机构信息

College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China.

Zhongshan Research Institute, Changchun University of Science and Technology, Zhongshan, China.

出版信息

J Biophotonics. 2023 Jul;16(7):e202300020. doi: 10.1002/jbio.202300020. Epub 2023 Apr 11.

Abstract

Objective and automatic clinical discrimination of normal and necrotic sites of small intestinal tissue remains challenging. In this study, hyperspectral imaging (HSI) and unsupervised classification techniques were used to distinguish normal and necrotic sites of small intestinal tissues. Small intestinal tissue hyperspectral images of eight Japanese large-eared white rabbits were acquired using a visible near-infrared hyperspectral camera, and K-means and density peaks (DP) clustering algorithms were used to differentiate between normal and necrotic tissue. The three cases in this study showed that the average clustering purity of the DP clustering algorithm reached 92.07% when the two band combinations of 500-622 and 700-858 nm were selected. The results of this study suggest that HSI and DP clustering can assist physicians in distinguishing between normal and necrotic sites in the small intestine in vivo.

摘要

客观且自动地对小肠组织的正常和坏死部位进行临床区分仍然具有挑战性。在这项研究中,使用高光谱成像 (HSI) 和无监督分类技术来区分小肠组织的正常和坏死部位。使用可见近红外高光谱相机获取了八只日本大耳白兔的小肠组织高光谱图像,并使用 K-均值和密度峰 (DP) 聚类算法来区分正常组织和坏死组织。本研究中的三个案例表明,当选择 500-622nm 和 700-858nm 这两个波段组合时,DP 聚类算法的平均聚类纯度达到了 92.07%。本研究结果表明,HSI 和 DP 聚类可协助医生在体内区分小肠的正常和坏死部位。

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