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.

翻译：近年来，高光谱成像在医学领域的应用日益普遍。研究者开发医学高光谱算法时面临的主要障碍之一是缺乏特定、公开可用的高光谱医学数据。本文所述工作是在欧洲HELICoiD（高光谱成像癌症检测）项目框架内开展的，该项目的主要目标是在神经外科手术过程中实时应用高光谱成像技术描绘脑肿瘤。本文提出了一种构建首个人脑活体组织高光谱数据库的方法。数据采用定制化高光谱采集系统获取，该系统能够捕捉400至1000纳米可见光与近红外（VNIR）波段的信息。针对连续拍摄同一场景的两幅图像进行了重复性评估，分析表明系统在450至900纳米光谱范围内工作效率更优。共获得来自22位患者的36幅高光谱图像，通过基于光谱角映射算法的半自动标注方法，从这些数据中标记了超过30万个光谱特征。定义了四种类别：正常组织、肿瘤组织、血管和背景元素。所有高光谱数据已公开存储在公共数据库中。