Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.

Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging.

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

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.

Perspectives of the Biomedical Engineering Program at UASLP after ten years - analysis and criticism.

The Biomedical Engineering (BME) bachelor pro-gram of the Faculty of Sciences in Universidad Autónoma de San Luis Potosí (UASLP) was created in June of 2010, with the aim of training professionals with an integral perspective in the engineering field by considering a multidisciplinary approach to develop and apply technology in the areas of medicine and biology. After 10 years, our BME program has achieved national recognition. Despite of being an emerging program, this achievement has been obtained by the consolidation of our academic staff, the outstanding participation of our students in national and international academic events, and the historical graduation results.

Multispectral Imaging for Hemoglobin Estimation by PCA.

Tissular blood perfusion is helpful to assess the health condition of a subject and even monitor superficial lesions. Current state of the art is focused on developing non-invasive, quantitative and accessible methods for blood flow monitoring in large areas. This paper presents an approach based on multispectral images on the VIS-NIR range to quantify blood perfusion.

Extended Blind End-member and Abundance Estimation with Spatial Total Variation for Hyperspectral Imaging.

Blind linear unmixing (BLU) methods allow the separation of multi and hyperspectral data into end-members and abundance maps in an unsupervised fashion. However, due to incident noise, the abundance maps can exhibit high presence of granularity. To address this problem, in this paper, we present a novel proposal for BLU that considers spatial coherence in the abundance estimations, through a total spatial variation component.