Leukocytes Detection, Classification and Counting in Smears of Peripheral Blood (original) (raw)
Related papers
Automated Classification of Normal and Abnormal Leukocytes
Journal of Histochemistry & Cytochemistry, 1974
The development of an automated system for counting and classifying normal and abnormal leukocytes in peripheral blood smears is described. General requirements are discussed and the results of a simulation experiment are presented. A sample of 1572 leukocytes, divided equally among 17 types, was photographed and analyzed using computerized pattern recognition techniques. Various geometrical, color and texture parameters were extracted from the cell images and an optimal set of 20 were used in several computerized classification runs. Training on one-half of the sample and classifying the other half resulted in an over-all correct classification of between 67 and 77% depending on the definition of classification error. When only normal cells are considered, correct classification is obtained for 9l.5% of the cells.
Automatic classification of leukocytes
Journal of Automatic Control, 2006
This paper presents a novel algorithm for the automatic compilation of differential blood count (DBC), which is based on the direct analysis of a blood smear image and artificial neural networks. The results of the algorithm testing show high sensitivity of the algorithm in leukocyte detection and classification accuracy of 86%. Also, the algorithm enables the detection of potentially falsely classified leukocytes and in that way, with the help of a hematological expert, enables additional increase in the DBC compilation quality.
Selection of the best features for leukocytes classification in blood smear microscopic images
Medical Imaging 2014: Digital Pathology, 2014
Automatic differential counting of leukocytes provides invaluable information to pathologist for diagnosis and treatment of many diseases. The main objective of this paper is to detect leukocytes from a blood smear microscopic image and classify them into their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte using features that pathologists consider to differentiate leukocytes. Features contain color, geometric and texture features. Colors of nucleus and cytoplasm vary among the leukocytes. Lymphocytes have single, large, round or oval and Monocytes have singular convoluted shape nucleus. Nucleus of Eosinophils is divided into 2 segments and nucleus of Neutrophils into 2 to 5 segments. Lymphocytes often have no granules, Monocytes have tiny granules, Neutrophils have fine granules and Eosinophils have large granules in cytoplasm. Six color features is extracted from both nucleus and cytoplasm, 6 geometric features only from nucleus and 6 statistical features and 7 moment invariants features only from cytoplasm of leukocytes. These features are fed to support vector machine (SVM) classifiers with one to one architecture. The results obtained by applying the proposed method on blood smear microscopic image of 10 patients including 149 white blood cells (WBCs) indicate that correct rate for all classifiers are above 93% which is in a higher level in comparison with previous literatures.
Image Processing Approach for Detection of Leukocytes in Peripheral Blood Smears
Journal of Medical Systems, 2019
Peripheral blood smear analysis is a gold-standard method used in laboratories to diagnose many hematological disorders. Leukocyte analysis helps in monitoring and identifying health status of a person. Segmentation is an important step in the process of automation of analysis which would reduce the burden on hematologists and make the process simpler. The segmentation of leukocytes is a challenging task due to variations in appearance of cells across the slide. In the proposed study, an automated method to detect nuclei and to extract leukocytes from peripheral blood smear images with color and illumination variations is presented. Arithmetic and morphological operations are used for nuclei detection and active contours method is for leukocyte detection. The results demonstrate that the proposed method detects nuclei and leukocytes with Dice score of 0.97 and 0.96 respectively. The overall sensitivity of the method is around 96%.
Computer Aided System for Leukocytes Classification and Segmentation in Blood Smear Images
2016 International Conference on Frontiers of Information Technology (FIT), 2016
Detection and counting of white blood cells (WBC) in blood samples provides valuable information to medical specialists, helping them to evaluate a wide range of important hematic pathologies such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and time consuming. An automatic detection and classification of WBC images can enhance the accuracy and speed up the detection of WBCs. In this paper, we propose an efficient framework for localization of WBCs within microscopic blood smear images using a multi-class ensemble classification mechanism. In the proposed framework, the nuclei are first segmented, followed by extraction of features such as texture, statistical, and wavelet features. Finally, the detected WBCs are classified into five classes including basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on a natural (non-synthetic) benchmark database validate the effectiveness and efficiency of the proposed system in contrast to state-of-the-art schemes.
SET INTERNATIONAL JOURNAL OF BROADCAST ENGINEERING, 2019
More than half of the medical decisions rely on laboratory tests, which are essential to complete diagnosis or to refer the patient to more specific tests. In this context, the blood count is the most requested laboratory test. Even though there is a wide variety of CBC equipment, many are similar in cost. The automated methodologies present high speed and high accuracy. However, the high cost is often incompatible with the cost of living of people living in less-favored countries. In this way, it is essential to develop methodologies that reduce the cost of the blood count. The present study builds on the development of a laboratory medical algorithm for the detection and counting of erythrocytes and leukocytes in digital blood smear images. The algorithm employs the Hough Transform and the detection of objects by coloration. The deployment and performance analysis of the algorithm were performed in the virtual environment of Matlab software. The experiments were conducted through 10 digital images from open-access platforms with later analysis of sample execution times through the "tic toc" function. The results of the quantifications were expressed separately. The methodology developed showed high accuracy (90%) as well as low time to execute each of the images analyzed, with the average execution time being less than 2 seconds. Therefore, this study can be considered the first step in the accomplishment of hemograms with low cost, greater accessibility, and speed without the loss of reliability of the method.
ABSTRACT The differential counting of white blood cell provides invaluable information to pathologist for diagnosis and treatment of many diseases manually counting of white blood cell is a tiresome, time-consuming and susceptible to error procedure due to the tedious nature of this process, an automatic system is preferable in this automatic process, segmentation and classification of white blood cell are the most important stages. The objective of the present study is to develop an automatic tool to identify and classify the white blood cells namely, lymphocytes, monocytes and neutrophil in digital microscopic images. We have proposed color based segmentation method and the geometric features extracted for each segment are used to identify and classify the different types of white blood cells. The experimental results are compared with the manual results obtained by the pathologist and demonstrate the efficacy of the proposed method.
Recent Trends in Intensive Computing
Healthcare services are an important part of human beings and healthcare services are changing with new and innovative technologies. In recent day’s healthcare sector performing very crucial role in metamorphose of traditional health services to e-health technologies. This proposal provides an error-free and improved technology-based blood analysis service for the identification of leucocytes in blood samples of humans. Leucocytes play a vital and important character in human immune systems. This system helps to protect the body from suffering from leukemia. Leukemia, a blood cancer, nowadays is commonly found in all age persons. Leukemia is a type of disease and image processing techniques and algorithms can play a crucial role in disease diagnostic methodology. Identification of leukocytes in blood smear provides important information to pathologist as well as doctors to analyze and predicts different types of diseases, such as cancer. However, this analysis is critical and major ...
Automatic recognition of five types of white blood cells in peripheral blood
Computerized Medical Imaging and Graphics, 2011
This paper proposes image processing algorithms to recognize five types of white blood cells in peripheral blood automatically. First, a method based on Gram-Schmidt orthogonalization is proposed along with a snake algorithm to segment nucleus and cytoplasm of the cells. Then, a variety of features are extracted from the segmented regions. Next, most discriminative features are selected using a Sequential Forward Selection (SFS) algorithm and performances of two classifiers, Artificial Neural Network (ANN) and Support Vector Machine (SVM), are compared. The results demonstrate that the proposed methods are accurate and sufficiently fast to be used in hematological laboratories.
Classification of Leukocyte Images Using K-Means Clustering Based on Geometry Features
—Information about counts and percentages of each type of leukocytes in blood is much needed to diagnose patients' illness. To gain that information, some functional enhancements had been applied to the optical microscopes so that they could produce digital images. Output images from these engineered microscopes were then extracted to get feature values from each image. These feature values then became input sets to the method of K-Means Clustering so that the leukocyte images could be classified according to each own cluster. Generally, the leukocyte classification process is conducted through four phases, which are image pre-processing, leukocyte segmentation, feature extraction, and leukocyte classification. Leukocyte types which were classified in this research were neutrophil, lymphocyte, monocyte, and eosinophil. Experiments were conducted using five kinds of features, which are normalized area, circularity, eccentricity, normalized parameter, and solidity, and by varying their types and their significant influences. The purpose of these trials were to determine which feature types would result in the highest value of accuracy and the effects of adding these respective features to the resulted accuracy. Based on the conducted classification results, it was found that the highest accuracy value was reached by circularity feature, which was 67%, meanwhile the lowest accuracy value was produced by the eccentricity feature, which was 43%. In this research, it was concluded that the accuracy value is ultimately determined by selecting the correct feature type rather than adding more features.