Determination of blood components (WBCs, RBCs, and Platelets) count in microscopic images using image processing and analysis

Cruz, Jennifer C. Dela; Jennifer, C.; Valiente,; Castor, Leonardo C.; Mendoza, Celine Margaret T.; Jay, B. Arvin; Jane, L. Song Cherry; Brian, Price

doi:10.1109/hnicem.2017.8269515

Cited by 30 publications

(19 citation statements)

References 3 publications

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“…In general, there are generally two different approaches in the automated counting process of blood cells. They are the image processing approach [1,3,[10][11][12] and the machine learning approach [2,4,[13][14][15].…”

Section: Related Workmentioning

confidence: 99%

“…Cruz et al [1] presented an image processing system to count blood cells. They used hue, saturation, value thresholding method, and connected component labelling for the identification and counting of blood cells.…”

Section: Related Workmentioning

confidence: 99%

“…A complete blood cell (CBC) count is an important test often requested by medical professionals to evaluate health condition [1,2]. The main three types of cells that constitute blood are red blood cells (RBCs), white blood cells (WBCs), and platelets.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning approach of automatic identification and counting of blood cells

Alam

Islam

2019

Healthcare Technology Letters

118

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A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using ‘you only look once’ (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalised. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Machine learning approach of automatic identification and counting of blood cells

Alam

Islam

2019

Healthcare Technology Letters

118

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“…However, they reported addressing large variations of blood cells and low quality images in their future work. Cruz et al [57] proposed RBC counting method using blob analysis based on HSV component and WT and obtained an average accuracy of 95.6% for 10 blood samples taken with 40X and 100X magnifications. Segmentation of RBCs from PBS images using Hough Transform (HT) was reported by many research groups [14,72,83,110,113,149,155].…”

Section: Thresholding and Transform-based Segmentation Methodsmentioning

confidence: 99%

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Navya

Prasad

Singh

2022

Med Biol Eng Comput

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Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444–454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world’s second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations. Graphical abstract

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“…A normal platelet has a diameter of 2-3 μm. A complete blood count is an important test for medical professionals to assess the health conditions [16,17]. Because of the large number of blood cells, traditional manual blood cell counting systems using hemocytometers have a high error rate and are time-consuming, and the accuracy in most cases depends heavily on the skills of the clinical laboratory analyst [18,19].…”

Section: Problem Descriptionmentioning

confidence: 99%

Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method

Chen

Tsai

2022

BMC Bioinformatics

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Background Researchers have tried to identify and count different blood cells in microscopic smear images by using deep learning methods of artificial intelligence to solve the highly time-consuming problem. Results The three types of blood cells are platelets, red blood cells, and white blood cells. This study used the Resnet50 network as a backbone network of the single shot detector (SSD) for automatically identifying and counting different blood cells and, meanwhile, proposed a systematic method to find a better combination of algorithm hyperparameters of the Resnet50 network for promoting accuracy for identifying and counting blood cells. The Resnet50 backbone network of the SSD with its optimized algorithm hyperparameters, which is called the Resnet50-SSD model, was developed to enhance the feature extraction ability for identifying and counting blood cells. Furthermore, the algorithm hyperparameters of Resnet50 backbone networks of the SSD were optimized by the Taguchi experimental method for promoting detection accuracy of the Resnet50-SSD model. The experimental result shows that the detection accuracy of the Resnet50-SSD model with 512 × 512 × 3 input images was better than that of the Resnet50-SSD model with 300 × 300 × 3 input images on the test set of blood cells images. Additionally, the detection accuracy of the Resnet50-SSD model using the combination of algorithm hyperparameters got by the Taguchi method was better than that of the Resnet50-SSD model using the combination of algorithm hyperparameters given by the Matlab example. Conclusion In blood cell images acquired from the BCCD dataset, the proposed Resnet50-SSD model had higher accuracy in identifying and counting blood cells, especially white blood cells and red blood cells.

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Determination of blood components (WBCs, RBCs, and Platelets) count in microscopic images using image processing and analysis

Cited by 30 publications

References 3 publications

Machine learning approach of automatic identification and counting of blood cells

Machine learning approach of automatic identification and counting of blood cells

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method

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