Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy ($OACC$) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the $OACC$ is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.

Hyperspectral imaging (HSI) has a crucial role in material classification tasks. The primary type of sensors in this field are linescan sensors, offering a high spatial and spectral resolution and improving the quality of the captured hypercubes. However, these cameras have inherent problems such as the need of dedicated hardware to move the camera over the scene and the necessity of pre-processing the raw captures in order to obtain the actual hypercube. This work makes an analysis and an adaptation of stitching techniques to apply them to the medical area. Also, it includes different methods to post-process the hypercubes obtained and it analyzes the SURF algorithm to improve processing times. The results obtained show that a stitching technique based on SURF is suitable for the medical area.

Uyperspectral imaging (HSI) has been adopted during the last years in different applications where material classification plays an important role. This favoured the improvement and development of new HS sensors, leading to HS snapshot cameras, where the sensor is able to acquire images in a realtime video fashion, reducing the spatial and spectral resolution for these cameras. However, although these cameras are able to acquire RAW HS images at high frames per second (FPS) rates, it is necessary to pre-process them to obtain an actual HS image and then obtain a classification map. This work addresses the development of an embedded CPU+GPU HS video classification system able to acquire and classify HS images in real-time video, i.e., at more than 25 FPS. Also, it includes a comparison where three different CPU+GPU embedded platforms are tested: NVIDIA Jetson Nano, NVIDIA Jetson TX1 and NVIDIA Jetson TX2. The results obtained show the feasibility of a HS video classification system using these embedded platforms.

Gliomas constitute a significant challenge in neurosurgery due to their high incidence and poor prognosis. Despite advancements in tumor detection techniques using machine learning approach with hyperspectral imaging, accurately distinguishing between healthy and tumoral tissues remains challenging. Following this trend, this paper introduces Libra, a low spectral resolution classifier designed for brain tumor detection, leveraging ensemble learning techniques to enhance classification performance. Evaluated across the Helicoid and Slim Brain databases, Libra demonstrates superior tumor sensitivity and a notable increase in accuracy compared to standalone SVM and the state-of-the-art Helicoid classification chain. While facing challenges in accurately distinguishing between blood vessels and tumoral tissues, Libra performs 32 % better than Helicoid in tumor sensitivity and 25.1 % in tumor F1-score. Moreover, when using unseen data, Libra demonstrates notable improvements of 7.8 % and 6.9 % respectively. These improvements are obtained using low complexity algorithms exploiting ensemble learning techniques.