Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.

Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459× and ~429× compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.

Blood perfusion parameters can be used to evaluate the micro-circulatory health condition of a patient. Several non-invasive optical techniques have been used to estimate blood perfusion as near-infrared spectroscopy or pulse-oximetry. However, these techniques require contact with the patient, and the measurements are restricted to a single point evaluation. These disadvantages could be solved by multispectral imaging. Hence, this paper presents an approach based on multispectral imaging and blind linear unmixing, as an alternative to estimate blood perfusion parameters in the hand palm. This work evaluated changes in oxygenated and deoxygenated hemoglobin concentrations by employing an experimental occlusion protocol in healthy volunteers. We compared the results of several blind linear unmixing and linear regression models. The average cosine similarity values between the prediction model and the photoplethysmography estimations varied in the range 87% and 96%. The mean R-squared adjusted values for oxygenated and deoxygenated hemoglobin were greater or equal than 0.75 and 0.84, respectively. Our results demonstrated the feasibility of non-invasive estimation of hemoglobin in the hand palm, and opened the possibility for calculating other perfusion parameters that help to diagnose and monitor pathologies in large tissue regions.

Today, hyperspectral (HS) imaging has become a powerful tool to identify remotely the composition of an interest area through the joint acquisition of spatial and spectral information. However, like in most imaging techniques, unwanted effects may occur during data acquisition, such as noise, changes in light intensity, temperature differences, or optical variations. In HS imaging, these problems can be attenuated using a reflectance calibration stage and optical filtering. Nevertheless, optical filtering might induce some distortion that could complicate the posterior image processing stage. In this work, we present a new proposal for reflectance calibration that compensates for optical alterations during the acquisition of an HS image. The proposed methodology was evaluated on an HS image of synthetic squares of various materials with specific spectral responses. The results of our proposal show high performance in two classification tests using the K-means algorithm with 97% and 88% accuracy; in comparison with the standard reflectance calibration from the literature that obtained 77% and 64% accuracy. These results illustrate the performance gain of the proposed formulation, which besides maintaining the characteristic features of the compounds within the HS image, keeps the resulting reflectance into fixed lower and upper bounds, which avoids a post-calibration normalization step.