Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1

Detección de trayectorias y reconocimiento de objetos regulares para el control por visión artificial de un robot móvil

Este trabajo terminal consiste en desarrollar el control por visión artificial de un prototipo de sistema autónomo, el cual tendrá la capacidad de interactuar en un ambiente controlado. El proyecto consistirá en un robot móvil el cual transitará en un entorno controlado, es decir donde el espacio, la iluminación y otros factores sean siempre los mismos. Se tendrá paredes negras y objetos blancos (esferas, pirámides y cubos), los cuales estarán colocados a diferentes distancias unos de otros. Como sensor se empleará una cámara web, ya que servirá para proporcionar información del entorno, la cámara estará acoplada al robot y se conectara a la computadora (Lap-Top), mediante la interfaz de comunicación USB. El camino que seguirá el robot móvil estará dado por las paradas negras, los objetos blancos y una línea roja la cual te servirá de guía al vehículo. El robot tendrá que detenerse en cada blanco para reconocer que tipo de figura esta captando la cámara, cuando tenga una pared enfrente de él, la evitará y continuará con su camino hasta llegar a la meta, finalmente desplegará en el computador cuantos objetos de cada clase encontró

Analysis of Random Local Descriptors in Face Recognition

This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition