The use of hyperspectral imaging for medical applications is becoming more
common in recent years. One of the main obstacles that researchers find when
developing hyperspectral algorithms for medical applications is the lack of
specific, publicly available, and hyperspectral medical data. The work
described in this paper was developed within the framework of the European
project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main
goal the application of hyperspectral imaging to the delineation of brain
tumors in real-time during neurosurgical operations. In this paper, the
methodology followed to generate the first hyperspectral database of in-vivo
human brain tissues is presented. Data was acquired employing a customized
hyperspectral acquisition system capable of capturing information in the Visual
and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed
for the cases where two images of the same scene were captured consecutively.
The analysis reveals that the system works more efficiently in the spectral
range between 450 and 900 nm. A total of 36 hyperspectral images from 22
different patients were obtained. From these data, more than 300 000 spectral
signatures were labeled employing a semi-automatic methodology based on the
spectral angle mapper algorithm. Four different classes were defined: normal
tissue, tumor tissue, blood vessel, and background elements. All the
hyperspectral data has been made available in a public repository.

该研究论文介绍了在欧洲项目 HELIcoiD（高光谱成像癌症检测）的框架内开发了第一个活体人类脑组织高光谱数据库的方法，该数据库中包含了用于在神经外科手术期间实时描绘脑肿瘤的高光谱图像和超过 300,000 个光谱标记。