Morphological characterization of the hippocampus: a first database in Ecuador

Introduction

The hippocampal volume is a well-known biomarker to detect and diagnose neurological, psychiatric, and psychological diseases. However, other morphological descriptors are not analyzed. Furthermore, not available databases, or studies, were found with information related to the hippocampal morphology from Latin-American patients living in the Andean highlands.

Methods

The hippocampus is manually segmented by two medical imaging specialists on normal brain magnetic resonance images. Then, its morphological qualitative and quantitative descriptors (volume, sphericity, roundness, diameter, volume-surface ratio, and aspect ratio) are computed via 3D digital level-set-based mathematical representation. Furthermore, other morphological descriptors and their possible correlation with the hippocampal volume is analyzed.

Results

We introduce a first database with the hippocampus' morphological characterization of 63 patients from Quito, Ecuador, male and female, aged between 18 and 95 years old.

Discussion

This study provides new research opportunities to neurologists, psychologists, and psychiatrists, to further understand the hippocampal morphology of Andean and Latin American patients.

MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.