Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images

Bhavnani, Lata A.; Jaliya, U. K.

doi:10.5815/ijigsp.2016.11.05

Cited by 14 publications

(8 citation statements)

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“…The methods of analysis of the textures are essentially based on the study of the relationships between each pixel and its neighbors for the fine textures, and on the spatial distribution of the levels of gray. These methods give only statistical [10] information on the images unlike methods such as the segmentation that they give visual information.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

Bouzid-Daho¹,

Boughazi²

2018

IJIGSP

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The aim of our work is to obtain a maximum rate of recognition of abnormal (cancerous) blood cells. We propose the development of a system based on kmeans methods, after an RGB channel decomposition by applying the algorithm which can segment our microscopic medical images. It turns out that the proposed system shows better segmentation and classification for the identification and detection of leukemia. The experimental results obtained are very encouraging, which helps hematologists to monitor the evolution of cancerous blood cells and make a good diagnosis.

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Section: Methodsmentioning

confidence: 99%

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

Bouzid-Daho¹,

Boughazi²

2018

IJIGSP

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“…Image processing and computer vision methods are utilized for counting purposes. Isolated blood cells are counted via automated algorithms rather than manual, which enhances accuracy ( Abbas, 2015 ; Bhavnani, Jaliya & Joshi, 2016 ; Miao & Xiao, 2018 ).…”

Section: Review Overviewmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

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“…A threshold method was also proposed by other researchers. [8][9][10][11][12] recommended a watershed-based and Otsu threshold-based segmentation which resulted in 99.3% and 93.3% accuracy, respectively. K-means clustering was another famous segmentation approach.…”

Section: Literature Reviewmentioning

confidence: 99%

WBC-based segmentation and classification on microscopic images: a minor improvement

Lam

Yoong

et al. 2021

F1000Res

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Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no robust automated method to segment and classify WBCs images with high accuracy. This paper aims to improve on WBCs image segmentation and classification method. Methods A triple thresholding method was proposed to segment the WBCs; meanwhile, a convolutional neural network (CNN)-based binary classification model that adopts transfer learning technique was proposed to detect and classify WBCs as a healthy or a malignant. The input dataset of this research work is the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). The process first converts the captured microscope images into HSV format for obtaining the H component. Otsu thresholding is applied to segment the WBC area. A 13 × 13 kernel with two iterations was used to apply morphological opening on image to ameliorate output results. Collected cell masks were used to detect the contour of each cell on the original image. To classify WBCs into a healthy or a malignant category, characteristics and conditions of WBCs are to be examined. A transfer learning technique and pre-trained InceptionV3 model were employed to extract the features from the images for classification. Results The proposed WBCs segmentation method yields 90.45% accuracy, 83.81% of the structural similarity index, 76.25% of the dice similarity coefficient, and is computationally efficient. The accuracy of fine-tuned classifier model for training, validation and test sets are 93.27%, 92.31% and 96.15% respectively. The obtained results are high in accuracy and precision are over 96% and with lower loss value. Discussion Triple thresholding outperforms K-means clustering in segmenting smaller dataset. Pre-trained InceptionV3 model and transfer learning improve the flexibility and ability of classifier.

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Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images

Cited by 14 publications

References 1 publication

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

A review of microscopic analysis of blood cells for disease detection with AI perspective

WBC-based segmentation and classification on microscopic images: a minor improvement

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