Hyperspectral (HS) imaging (HSI) technology combines the main features of two existing technologies: imaging and spectroscopy. This allows to analyse simultaneously the morphological and chemical attributes of the objects captured by a HS camera. In recent years, the use of HSI provides valuable insights into the interaction between light and biological tissues, and makes it possible to detect patterns, cells, or biomarkers, thus, being able to identify diseases. This work presents the HistologyHSI-GB dataset, which contains 469 HS images from 13 patients diagnosed with brain tumours, specifically glioblastoma. The slides were stained with haematoxylin and eosin (H&E) and captured using a microscope at 20× power magnification. Skilled histopathologists diagnosed the slides and provided image-level annotations. The dataset was acquired using custom HSI instrumentation, consisting of a microscope equipped with an HS camera covering the spectral range from 400 to 1000 nm.

Hyperspectral (HS) imaging (HSI) expands the number of channels captured within the electromagnetic spectrum with respect to regular imaging. Thus, microscopic HSI can improve cancer diagnosis by automatic classification of cells. However, homogeneous focus is difficult to achieve in such images, being the aim of this work to automatically quantify their focus for further image correction. A HS image database for focus assessment was captured. Subjective scores of image focus were obtained from 24 subjects and then correlated to state-of-the-art methods. Maximum Local Variation, Fast Image Sharpness block-based Method and Local Phase Coherence algorithms provided the best correlation results. With respect to execution time, LPC was the fastest.

Hyperspectral Imaging (HSI) is increasingly adopted in medical applications for the usefulness of understanding the spectral signature of specific organic and non-organic elements. The acquisition of such images is a complex task, and the commercial sensors that can measure such images is scarce down to the point that some of them have limited spatial resolution in the bands of interest. This work proposes an approach to enhance the spatial resolution of hyperspectral histology samples using super-resolution. As the data volume associated to HSI has always been an inconvenience for the image processing in practical terms, this work proposes a relatively low computationally intensive algorithm. Using multiple images of the same scene taken in a controlled environment (hyperspectral microscopic system) with sub-pixel shifts between them, the proposed algorithm can effectively enhance the spatial resolution of the sensor while maintaining the spectral signature of the pixels, competing in performance with other state-of-the-art super-resolution techniques, and paving the way towards its use in real-time applications.

SPIE Digital Library Proceedings

Hyperspectral (HS) imaging (HSI) is a powerful image technique that allows capturing spatial and spectral information, being able to characterize materials, tissues, and elements in a non-invasive manner. HSI technology is well established at the macroscopic level, but there are still technical challenges to overcome before it can be applied to the microscopic world, such as the lack of standardized characterization methodologies to HS microscopic systems that allow the correct data acquisition as well as ensure the repeatability of the experiments. In this work, we propose a comprehensive roadmap for characterizing and validating such systems, integrating essential parameters highlighted in the current state of the art. Furthermore, we provide a list of the materials needed for their characterization and testing the methodology on two different HS microscopic systems chosen as representative of common configurations in the field, where a HS camera is integrated into a bright-field microscope. Our proposed roadmap assesses the following parameters: dynamic range, noise quantification, pixel size, spatial frequency response, spatial scanning accuracy, spatial repeatability, flat-field correction, tone transfer, and spectral sensitivity. We address the challenge of unifying these parameters into a unified and standardized roadmap. All data used to characterize both systems have been captured by the authors. In summary, this comprehensive analysis provides a guideline for the scientific community to develop and characterize HS microscopic systems to ensure reliability, efficiency, and accuracy.

Recent advancements in multispectral (MS) and hyperspectral (HS) microscopy have focused on sensor and system improvements, yet sample processing remains overlooked. We conducted an analysis of the literature, revealing that 40% of studies do not report sample thickness. Among those that did report it, the vast majority, 98%, used 2–10 µm samples. This study investigates the impact of unstained sample thickness on MS/HS image quality through light transport simulations. Monte Carlo simulations were conducted on various tissue types (i.e., breast, colorectal, liver, and lung) using optical property parameters extracted from the literature. The simulations revealed that thin samples reduce tissue differentiation, while higher thicknesses (approximately 500 µm) improve discrimination, though at the cost of reduced light intensity. Although the results are based on idealized conditions and exclude certain real-world factors such as sample variability and instrument-specific effects, they highlight the need to study and optimize sample thickness for enhanced tissue characterization and diagnostic accuracy in MS/HS microscopy.