Rede neural convolucional eficiente para detecção e contagem dos glóbulos sanguíneos

Blood cell analysis is an important part of the health and immunity assessment. There are three major components of the blood: red blood cells, white blood cells, and platelets. The count and density of these blood cells are used to find multiple disorders like blood infections (anemia, leukemia, among others). Traditional methods are time-consuming, and the test cost is high. Thus, it arises the need for automated methods that can detect different kinds of blood cells and count the number of cells. A convolutional neural network-based framework is proposed for detecting and counting the cells. The neural network is trained for the multiple iterations, and a model having lower validation loss is saved. The experiments are done to analyze the performance of the detection system and results with high accuracy in the counting of the cells. The mean average precision is achieved when compared to ground truth provided to respective labels. The value of the average precision is found to be ranging from 70% to 99.1%, with a mean average precision value of 85.35%. The proposed framework had much less time complexity: it took only 0.111 seconds to process an image frame with dimensions of 640×480 pixels. The system can also be implemented in low-cost, single-board computers for rapid prototyping. The efficiency of the proposed framework to identify and count different blood cells can be utilized to assist medical professionals in finding disorders and making decisions based on the obtained report.El análisis de células sanguíneas es una parte importante de la evaluación de la salud y la inmunidad. Hay tres componentes principales de los glóbulos rojos, los glóbulos blancos y las plaquetas. El recuento y la densidad de estas células sanguíneas se utilizan para encontrar múltiples trastornos como infecciones de la sangre como anemia, leucemia, etc. Los métodos tradicionales consumen mucho tiempo y el costo de las pruebas es alto. Por tanto, surge la necesidad de métodos automatizados que puedan detectar diferentes tipos de células sanguíneas y contar el número de células. Se propone un marco basado en una red neuronal convolucional para la detección y el recuento de las células. La red neuronal se entrena para las múltiples iteraciones y se guarda un modelo que tiene una menor pérdida de validación. Los experimentos se realizan con el fin de analizar el rendimiento del sistema de detección y los resultados con alta precisión en el recuento de células. La precisión promedio se logra al analizar las respectivas etiquetas que hay en la imagen. Se ha determinado que el valor de la precisión promedio, oscila entre el 70% y el 99,1% con un valor medio de 85,35%. El coste computacional de la propuesta fue de 0.111 segundos, procesar una imagen con dimensiones de 640 × 480 píxeles. El sistema también se puede implementar en ordenadores con CPU de bajo costo, para la creación rápida de prototipos. La eficiencia de la propuesta, para identificar y contar diferentes células sanguíneas, se puede utilizar para ayudar a los profesionales médicos a encontrar los trastornos y la toma decisiones, a partir de la identificación automática.O exame de células sanguíneas é uma parte importante da avaliação de saúde e imunidade. Há três componentes principais dos glóbulos vermelhos, glóbulos brancos e plaquetas. A contagem e a densidade dessas células sanguíneas são usadas para encontrar múltiplos distúrbios, tais como infecções no sangue: anemia, leucemia, etc. Os métodos tradicionais são demorados e o custo dos testes é alto. Portanto, surge a necessidade de métodos automatizados que possam detectar diferentes tipos de células sanguíneas e contar o número de células. É proposta uma estrutura baseada em rede neural convolucional para a detecção e contagem de células. A rede neural é treinada para múltiplas iterações e é salvo um modelo que tem uma menor perda de validação. São realizados experimentos para analisar o desempenho do sistema de detecção e os resultados com alta precisão na contagem de células. A precisão média é obtida analisando os respectivos rótulos na imagem. Foi determinado que o valor médio de precisão oscila entre 70 % e 99,1 % com um valor médio de 85,35 %. O custo computacional da proposta foi de 0,111 segundos, processando uma imagem com dimensões de 640 × 480 pixels. O sistema também pode ser implementado em computadores com CPUs de baixo custo para prototipagem rápida. A eficiência da proposta, para identificar e contar diferentes células sanguíneas, pode ser usada para ajudar os profissionais médicos a encontrar distúrbios e tomar decisões, com base na identificação automática

Simultaneous Segmentation of Leukocyte and Erythrocyte in Microscopic Images Using a Marker-Controlled Watershed Algorithm

The density or quantity of leukocytes and erythrocytes in a unit volume of blood, which can be automatically measured through a computer-based microscopic image analysis system, is frequently considered an indicator of diseases. The segmentation of blood cells, as a basis of quantitative statistics, plays an important role in the system. However, many conventional methods must firstly distinguish blood cells into two types (i.e., leukocyte and erythrocyte) and segment them in independent procedures. In this paper, we present a marker-controlled watershed algorithm for simultaneously extracting the two types of blood cells to simplify operations and reduce computing time. The method consists of two steps, that is, cell nucleus segmentation and blood cell segmentation. An image enhancement technique is used to obtain the leukocyte marker. Two marker-controlled watershed algorithms are based on distance transformation and edge gradient information to acquire blood cell contour. The segmented leukocytes and erythrocytes are obtained simultaneously by classification. Experimental results demonstrate that the proposed method is fast, robust, and efficient

A PCNN Framework for Blood Cell Image Segmentation

This research presents novel methods for segmenting digital blood cell images under a Pulse Coupled Neural Network (PCNN) framework. A blood cell image contains different types of blood cells found in the peripheral blood stream such as red blood cells (RBCs), white blood cells (WBCs), and platelets. WBCs can be classified into five normal types – neutrophil, monocyte, lymphocyte, eosinophil, and basophil – as well as abnormal types such as lymphoblasts and others. The focus of this research is on identifying and counting RBCs, normal types of WBCs, and lymphoblasts. The total number of RBCs and WBCs, along with classification of WBCs, has important medical significance which includes providing a physician with valuable information for diagnosis of diseases such as leukemia. The approach comprises two phases – segmentation and cell separation – followed by classification of WBC types including detection of lymphoblasts. The first phase presents two methods based on PCNN and region growing to segment followed by a separate method that combines Circular Hough Transform (CHT) with a separation algorithm to find and separate each RBC and WBC object into separate images. The first method uses a standard PCNN to segment. The second method uses a region growing PCNN with a maximum region size to segment. The second phase presents a WBC classification method based on PCNN. It uses a PCNN to capture the texture features of an image as a sequence of entropy values known as a texture vector. First, the parameters of the texture vector PCNN are defined. This is then used to produce texture vectors for the training images. Each cell type is represented by several texture vectors across its instances. Then, given a test image to be classified, the texture vector PCNN is used to capture its texture vector, which is compared to the texture vectors for classification. This two-phase approach yields metrics based on the RBC and WBC counts, WBC classification, and identification of lymphoblasts. Both the standard and region growing PCNNs were successful in segmenting RBC and WBC objects, with better accuracy when using the standard PCNN. The separate method introduced with this research provided accurate WBC counts but less accurate RBC counts. The WBC subimages created with the separate method facilitated cell counting and WBC classification. Using a standard PCNN as a WBC classifier, introduced with this research, proved to be a successful classifier and lymphoblast detector. While RBC accuracy was low, WBC accuracy for total counts, WBC classification, and lymphoblast detection were overall above 96%