Blur-specific image quality assessment of microscopic hyperspectral images

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 fastestBlur-specific image quality assessment of microscopic hyperspectral imagespublishedVersio

Towards Real-Time Hyperspectral Multi-Image Super-Resolution Reconstruction Applied to Histological Samples

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Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications

SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples.

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200⁻3500 cm-1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.This work has been supported in part by the European Commission through the FP7 FET Open Programme ICT-2011.9.2, European Project HELICoiD “HypErspectral Imaging Cancer Detection” under Grant Agreement 618080. This work has been also supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project “Hyperespectral identification of Brain tumors” under Grant Agreement ProID2017010164. Additionally, this work has been supported in part by the 2016 PhD Training Program for Research Staff of the University of Las Palmas de Gran Canaria. Finally, this work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “Agencia Canaria de Investigacion, Innovacion y Sociedad de la Información (ACIISI)” of the “Conserjería de Economía, Industria, Comercio y Conocimiento” of the “Gobierno de Canarias”, which is part-financed by the European Social Fund (FSE) (POC 2014-2020, Eje 3 Tema Prioritario 74 (85%))

Hacia la implementación on the edge de un segmentador de PCG basado en la U-Net

A computer-aided cardiovascular diseases diagnostic system requires both accuracy and real-time response. This can be reached thanks to the deep model implementation on edge devices. This works introduces a reduction strategy for a heart sound segmentation model, and its impact in the implementation over low-spec FPGAs.Un sistema de asistencia al diagnóstico de enfermedades cardiovasculares requiere de precisión y respuesta en tiempo real, algo que se puede alcanzar gracias a la implementación de modelos deep learning on the edge. En este trabajo se presenta la reducción de un modelo para la segmentación de fonocardiogramas y su efecto en la implementación sobre FPGAs low-spec

Proceso de aprendizaje en la fabricación integrada de una plataforma robótica educativa multidisciplinar

[ES] La robótica educativa ha llegado a las aulas para quedarse. El aprendizaje STEAM (Science, Technology, Engineering, Arts and Mathematics) ha puesto en boga el uso de los robots en las clases pero, en su mayoría, son productos cerrados y a un alto precio. El proyecto de innovación educativa «Diseño, implementación y puesta en práctica de una plataforma modular de robótica educativa de bajo coste» de la Universidad de Las Palmas de Gran Canaria pretende diseñar un robot educativo abierto, modular y de bajo coste para hacer más accesible la robótica. Uno de los retos que pretende alcanzar es que dicho robot, gracias a su modularidad, sea capaz de adaptarse a cualquier nivel educativo, desde infantil hasta grados universitarios. Este estudio analiza el nivel de aceptación por parte de los estudiantes del Grado en Ingeniería en Diseño Industrial y Desarrollo de Productos, realizando un análisis de los robots educativos más usados en la actualidad en todos los niveles y comparándolos con la propuesta del proyecto. Además, se realizó una encuesta a 78 alumnos de ingeniería, concluyendo que muestran un interés general por la propuesta, pero no tanto entre los del grupo de estudio, probablemente debido a la falta de conocimientos de robótica.[EN] Educational robotics has come to the classrooms and is here to stay. STEAM (Science, Technology, Engineering, Arts and Mathematics) learning has put the use of robots in classrooms in vogue, though these are mostly closed products and at a high price. The educational innovation project “Design, implementation and tests of a modular low-cost educational robotics platform” from the University of Las Palmas de Gran Canaria, expects to design an open, modular and low-cost educational robot to make robotics more accessible. One of the challenges which the project hopes to accomplish is for this robot to be able to adapt to any educational level, thanks to its modularity, from pre-school to university levels. This study analyzes the Industrial Design and Product Development Engineering degree students’ level of acceptance. Therefore, an analysis of the currently most used educational robots at any level has been made, comparing them to the project design. Moreover, a survey was passed to a total 78 students from several degrees to compare the level of acceptance, concluding that students show a general interest in the proposal, but not so among those of the study group, probably due to the lack of robotics knowledge.Martí Gil, A.; Quevedo Gutiérrez, E.; Hernández Castellano, P.; Zapatera Llinares, A.; Fabelo Gómez, H.; Ortega Sarmiento, S.; Marrero Callicó, G. (2021). Proceso de aprendizaje en la fabricación integrada de una plataforma robótica educativa multidisciplinar. En IN-RED 2020: VI Congreso de Innovación Educativa y Docencia en Red. Editorial Universitat Politècnica de València. 550-564. https://doi.org/10.4995/INRED2020.2020.11960OCS55056

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

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Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology