Systematic segmentation method based on PCA of image hue features for white blood cell counting

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works

Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. -e proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.Comment: 13 pages, 13 figure

Lymphatic Filariasis detection in microscopic images

In Africa, the propagation of parasites like the lymphatic filariasis is complicatingseriously the efforts of health professionals to cure certain diseases. Although there aremedicines capable to treat the lymphatic filariasis, this needs to be discovered firstly which isnot always an easy task having into account that in most countries affected by this disease it canonly be detected at night (nocturne). The lymphatic filariasis is then, a parasitical infectionwhich can originate changes or ruptures in the lymphatic system as well as an abnormal growthof certain areas of the body causing pain, incapacity and social stigma.Approximately 1.23 billion people in 58 countries from all over the world are threatenedby this disease which requires a preventive treatment to stop its propagation which makes iteven more important for the existence of a mechanism that is less costly and more agile in theanalysis of a blood smear to verify the existence of microfilariae (little worms that are producedby other adult worms while housed in the lymphatic system).The lymphatic filariasis is caused by an infection with nematodes ("roundworms") of theFilariodidea family in which three types are inserted: Wuchereria Bancroft, responsible for 90%of all cases; Brugia Malayi, responsible for almost every remaining; B.Timori also causing thedisease. All three have characteristics that can differentiate them which allow them to beidentified.The current identification process of the disease consists on the analysis of microfilariae ina blood smear with a blood sample through a microscope and its identification by the observer.Taking this into account, it is intended to develop image analysis and processingtechniques for the recognition and counting of the two principal types of filarial worms from athin blood smear, a smartphone and a portable microscope making the detection possiblewithout the need of a health professional and consequent automation of the process. To makethis possible an adapter smartphone-microscope can be used to obtain an image with themagnification of 40x3. The images can then be analyzed in a server or in the smartphone, if ithas enough processing for it. It is expected from this process that the need to resort to labs toprocess the blood smear gets fulfilled making the process more accessible and agile instead ofcostly and slow.For the detection of the parasites from the acquired images it is intended to implement,experiment and choose the more adequate operations. These comprise pre-processing operationswith the goal to enhance the acquired images and eliminate possible artifacts prevenient fromthe acquisition system. However, the principal operations should be those that allow theverification of existence or nonexistence, recognition and classification of the pretendedparasites. Processing and analysis techniques that are common in these processes are based inthe extraction of features (e.g. SIRF, SURF, and FLANN) template similarity, edge detectionand description of contours and recognition of statistical patterns.Once detected and recognized one or more parasites and its types should be defined andused a rule to declare the presence of the disease and its stage

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks

Automatic Segmentation and Classification of Red and White Blood cells in Thin Blood Smear Slides

In this work we develop a system for automatic detection and classification of cytological images which plays an increasing important role in medical diagnosis. A primary aim of this work is the accurate segmentation of cytological images of blood smears and subsequent feature extraction, along with studying related classification problems such as the identification and counting of peripheral blood smear particles, and classification of white blood cell into types five. Our proposed approach benefits from powerful image processing techniques to perform complete blood count (CBC) without human intervention. The general framework in this blood smear analysis research is as follows. Firstly, a digital blood smear image is de-noised using optimized Bayesian non-local means filter to design a dependable cell counting system that may be used under different image capture conditions. Then an edge preservation technique with Kuwahara filter is used to recover degraded and blurred white blood cell boundaries in blood smear images while reducing the residual negative effect of noise in images. After denoising and edge enhancement, the next step is binarization using combination of Otsu and Niblack to separate the cells and stained background. Cells separation and counting is achieved by granulometry, advanced active contours without edges, and morphological operators with watershed algorithm. Following this is the recognition of different types of white blood cells (WBCs), and also red blood cells (RBCs) segmentation. Using three main types of features: shape, intensity, and texture invariant features in combination with a variety of classifiers is next step. The following features are used in this work: intensity histogram features, invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform features, Haralick and Tamura features. Next, different statistical approaches involving correlation, distribution and redundancy are used to measure of the dependency between a set of features and to select feature variables on the white blood cell classification. A global sensitivity analysis with random sampling-high dimensional model representation (RS-HDMR) which can deal with independent and dependent input feature variables is used to assess dominate discriminatory power and the reliability of feature which leads to an efficient feature selection. These feature selection results are compared in experiments with branch and bound method and with sequential forward selection (SFS), respectively. This work examines support vector machine (SVM) and Convolutional Neural Networks (LeNet5) in connection with white blood cell classification. Finally, white blood cell classification system is validated in experiments conducted on cytological images of normal poor quality blood smears. These experimental results are also assessed with ground truth manually obtained from medical experts

A Review of Conventional and Machine Learning Techniques for Malaria Parasite Detection Using a Thick Blood Smear

Life-threatening malaria is caused by parasites that are lethally effective and harmful and are transmitted through the bite of female Anopheles mosquitoes. In 2015, WHO reported more than 200 million deaths occurred because of this. This makes malaria one of the most vulnerable diseases. The Plasmodium parasite needs to be detected at the early stages for the patient’s survival. Microscopists over the years have been made such craftsmen that they through their expertise have been able to diagnose malaria, being followed by an area expansion support from computer-aided diagnosis. But the expertise required for feature extraction were questionable, which were later replaced by deep learning techniques through automatic feature extraction in CNN's. This paper provides a review of some such techniques and methods which were used for the said purposes

Sistema de conteo automático de células blancas en el plasma sanguíneo con información del color

Tesis de licenciatura. Aplicación de algoritmos de inteligencia artificial y tratamiento de imágenes para el conteo de células blancas en imágenes de la sangre.En el conteo automático y el reconocimiento de los sistemas de glóbulos blancos (WBC), utilizando imágenes de muestras de frotis de sangre (BS), el proceso de segmentación es una etapa importante porque la precisión del conteo y la clasificación depende, en cierta medida, de la precisión de la segmentación. Diferentes trabajos que abordan este problema se centran en algoritmos de segmentación basados en características de forma, pero estos algoritmos están diseñados, principalmente, para imágenes recortadas de WBC; es decir, la imagen de entrada contiene solo un WBC, mientras que generalmente en las imágenes BS contienen varios WBC. En este trabajo presentamos una propuesta para el conteo y reconocimiento de WBC en imágenes BS que contienen varios WBC dentro de la imagen, donde las contribuciones son: 1) una propuesta de segmentación que emula la percepción humana del color, donde los WBC están segmentados por la diferencia cromática con respecto a los otros elementos de la BS; 2) en las imágenes BS es común encontrar que los WBC están superpuestos, por lo tanto, presentamos un enfoque para separar los WBC superpuestos calculando sus diferencias de color, donde el tono y la intensidad se procesan por separado. Mostramos los resultados obtenidos al realizar experimentos con tres bases de datos de imágenes diferentes; Según los resultados obtenidos, afirmamos que nuestra propuesta es competitiva

A Review of Conventional and Machine Learning Techniques for Malaria Parasite Detection Using a Thick Blood Smear

Life-threatening malaria is caused by parasites that are lethally effective and harmful and are transmitted through the bite of female Anopheles mosquitoes. In 2015, WHO reported more than 200 million deaths occurred because of this. This makes malaria one of the most vulnerable diseases. The Plasmodium parasite needs to be detected at the early stages for the patient’s survival. Microscopists over the years have been made such craftsmen that they through their expertise have been able to diagnose malaria, being followed by an area expansion support from computer-aided diagnosis. But the expertise required for feature extraction were questionable, which were later replaced by deep learning techniques through automatic feature extraction in CNN's. This paper provides a review of some such techniques and methods which were used for the said purposes