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Proceedings ArticleDOI

An automated method for counting and characterizing red blood cells using mathematical morphology

TL;DR: One of the important findings is that the proposed method gives accurate count of red blood cells of the blood sample, and classifies each cell into one of the four categories mentioned above.
Abstract: This paper presents an automated method for counting red blood cells present in a blood sample. The proposed method addresses the problems of holes present in blood cells and overlapping characteristics of the red blood cells. The procedure is quite simple and straightforward, which utilizes mathematical morphological operations of erosion and dilation for performing different steps. It first thresholds a gray scale image to obtain the binary image using the Otsu thresholding method, and then, performs the hole filling process on the red blood cells if they have holes. Then, the process moves on to the job of counting the red blood cells. For this, each red blood cell is extracted and its shape analysis is performed to decide whether it is circular, non-circular, overlapping or just partially present in the sample. If a cell is only partially present in the image, then it is discarded. In case of overlapping, the number of cells in the overlapped area is determined. Several experimental results have been presented to establish the effectiveness of the method. One of the important findings is that the proposed method gives accurate count of red blood cells of the blood sample, and classifies each cell into one of the four categories mentioned above.
Citations
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Journal ArticleDOI
TL;DR: A new approach to the segmentation of cardiac fat from Computed Tomography (CT) images using a clustering algorithm called Floor of Log (FoL), which proves to be efficient, besides having good application times, and has the potential to be a medical diagnostic aid tool.

21 citations

Journal ArticleDOI
TL;DR: Technique has been introduced to count the RBCs automatically and images are classified on the basis of color, texture and morphology, namely elliptocytes, echinocytes, tear drop cells and macrocytes, which achieves overall accuracy of 91.667% and is computationally very efficient.

21 citations

Journal ArticleDOI
TL;DR: The proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model and shows that the cell's name classification prediction accuracy, sensitivity, specificity, and precision were achieved.
Abstract: Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.

20 citations


Cites background from "An automated method for counting an..."

  • ...The abnormal cells are the cells that cause anemia diseases [22]....

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Journal ArticleDOI
TL;DR: Segmentation and counting of RBCs and WBCs from microscopic blood sample images using Otsu’s thresholding and morphological operations and Circular Hough Transform are presented.
Abstract: In the biomedicine field, blood cell analysis is the first step for diagnosis of many of the disease. The first test that is requested by a doctor is the CBC (Complete Blood cell Count). Microscopic image of blood stream contains three types of blood cells: Red Blood Cells (RBCs), White Blood Cells (WBCs) and platelets. Earlier counting of blood cell was done manually which was inaccurate and depends on operator’s skill. Counting of blood cells using image processing provides cost effective and accurate result than manual counting. During the counting process, the splitting of clumped cell is the most challenging issue. This paper represents segmentation and counting of RBCs and WBCs from microscopic blood sample images. Segmentation is done using Otsu’s thresholding and morphological operations. Counting of cells is done using geometric features of cells. RBCs contain clumped cells which make the task of counting of cells accurately very challenging. For counting of RBCs, two different methods are used: 1) Watershed segmentation 2) Circular Hough Transform. Comparison of both this method is shown for randomly selected images. The performance of counting methods is also analyzed by comparing it with results obtained by manual counts.

13 citations

Journal ArticleDOI
TL;DR: The Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success and can be used for automatic classification of white blood cells.
Abstract: Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success. The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis of diseases for hematologists and internal medicine specialists.

13 citations


Cites background from "An automated method for counting an..."

  • ...Microscopic analysis of peripheral blood smear results in hematology is a costly and timeconsuming process (Krzyzak et al. 2011, Li et al. 2014, Maji et al. 2015)....

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References
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Journal ArticleDOI

37,017 citations


"An automated method for counting an..." refers methods in this paper

  • ...The Otsu method is a non parametric and unsupervised method of automatic threshold selection for picture segmentation [10]....

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BookDOI
27 Jan 2006
TL;DR: Self-contained text covering practical image processing methods and theory for image texture analysis, essential to a range of applications in areas as diverse as robotics, defence, medicine and the geo-sciences.
Abstract: DESCRIPTION Self-contained text covering practical image processing methods and theory for image texture analysis. Techniques for the analysis of texture in digital images are essential to a range of applications in areas as diverse as robotics, defence, medicine and the geo-sciences. In biological vision, texture is an important cue allowing humans to discriminate objects. This is because the brain is able to decipher important variations in data at scales smaller than those of the viewed objects. In order to deal with texture in digital data, many techniques have been developed by image processing researchers.

507 citations


"An automated method for counting an..." refers methods in this paper

  • ...The algorithm that has been used for hole filling is a completely automated procedure based on morphological reconstruction that involves morphological operations of geodesic dilation and erosion [8], [9]....

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  • ...The detected overlapping cells are then counted by using the morphological operation of binary erosion [8], [9]....

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Journal ArticleDOI
TL;DR: The state of the art regarding traditional and new parameters with emphasis on clinical applications and analytic quality are analyzed regarding the CBC count and leukocyte differential count.
Abstract: The CBC count and leukocyte differential count (LDC) are among the most frequently requested clinical laboratory tests. These analyses are highly automated, and the correct interpretation of results requires extensive knowledge of the analytic performance of the instruments and the clinical significance of the results they provide. In this review, we analyze the state of the art regarding traditional and new parameters with emphasis on clinical applications and analytic quality. The problems of some traditional parameters of the CBC count, such as platelet counts, some components of the LDC such as monocyte and basophil counts, and other commonly used indices such as red cell volume distribution width and platelet indices such as mean platelet volume and platelet distribution width are considered. The new parameters, evaluated from analytic and clinical viewpoints, are the available components of the extended differential count (hematopoietic progenitor cells, immature granulocytes, and erythroblasts), the immature reticulocyte fraction, the reticulocyte indices, the fragmented RBCs, and the immature platelet fraction.

339 citations


"An automated method for counting an..." refers background in this paper

  • ...Hence, blood cell counts are amongst the most commonly performed blood tests in medicine as they can provide an overview of the patient’s overall health status [1], [2]....

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Book
30 Sep 1998
TL;DR: This book explains how to establish a Cell Culture Lab, and some of the techniques used, and discusses contamination, how to avoid It, Recognize It, and Get Rid of It when It's There.
Abstract: 1.Introduction. 2.Setting up a Cell Culture Lab. 3.The Physical Environment. 4.Media. 5.Standard Cell Culture Techniques. 6.Looking at Cells. 7.Contamination, How to Avoid It, Recognize It, and Get Rid of It When It's There. 8.Special Considerations for Serum Free Culture-Established Cell Lines. 9.Primary Cultures. 10.Establishing a Cell Line. 11.Special Growth Conditions. 12.Cell Culture for Commercial Settings. Glossary. Appendices: Equipment List. List of Cell Lines and Where to Get Them. Preparation of Bovine Pituitary Extract. Index.

142 citations


"An automated method for counting an..." refers methods in this paper

  • ...To count the blood cells, the physician must view the hemacytometer through a microscope and count the RBCs using a hand tally counter [4]....

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01 Jan 2007
TL;DR: This research project aims to provide a software-based cost effective and an efficient alternative in recognizing and analyzing blood cells.
Abstract: Fast and cost-effective production of blood cell count reports is of paramount importance in the healthcare industry. The traditional method of manual count under the microscope yields inaccurate results and put an intolerable amount of stress on the Medical Laboratory Technicians. Although there are hardware solutions such as the Automated Hematology Counter, developing countries like Sri Lanka are not capable of deploying such prohibitively expensive machines in every hospital laboratory in the country. As a solution to this problem, this research project aims to provide a software-based cost effective and an efficient alternative in recognizing and analyzing blood cells.

25 citations


"An automated method for counting an..." refers methods in this paper

  • ...But, in this method, no special attention is given to overlapping cells or cells with holes [7]....

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