Overlapping White Blood Cells Detection Based on Watershed Transform and Circle Fitting

Sukhia, Komal Nain; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Iltaf, Naima

doi:10.13164/re.2017.1177

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…This design [8] uses edge map extraction and segmentation for separating the overlapped WBCs and for detecting both overlapped and separated WBCs using parametric circle approximation, in which they use watershed algorithm for detecting WBCs. In this proposed system [9] for segmentation of WBC they use standard ELM classification Technique formed on fast relevant vector-machine(Fast-RVM), by fitting the histogram by RVM by Fast relevance vector.…”

Section: Related Workmentioning

confidence: 99%

Classification of WBC cell classification using fully connected convolution neural network

Kannan¹,

Babu²,

Praveena³

et al. 2023

J. Phys.: Conf. Ser.

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White blood cells (WBCs) are cells that is key factor of the immune systems which is help to our body fight off contagions and other diseases. In order to enhance the diagnosis of various diseases in medical field by using image processing techniques from the blood cells. In that, Leukemia is associated with one type of cancer of the blood and bone marrow. It is look like spongy tissue inside the bones where blood cells are made. In this paper, a fully connected. Convolution neural network is used to segmented and classification of blood cell microscope WBC images for healthy and unhealthy conditions. The performance of the classifier was analyzed. The accuracy sensitivity specificity and pression are 96.84%, 96.26%,97.35% and 96.39% respectively.

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Section: Related Workmentioning

confidence: 99%

Classification of WBC cell classification using fully connected convolution neural network

Kannan¹,

Babu²,

Praveena³

et al. 2023

J. Phys.: Conf. Ser.

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“…A dataset of 31 peripheral blood smear images from a local dataset was utilized to test this approach, and they achieved a sensitivity of 100% and an average accuracy of 97.52%. The research paper [21]…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Wady¹

2022

UHD J SCI TECH

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Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.

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“…Finally, in 2017, Nain et al [13] proposed a system to differentiate individual and overlapped WBCs based on the watershed segmentation scheme to segment the cells. The edge map extraction technique, which is a circle fitted on each cell, is used to identify both individual and overlapped cells.…”

Section: Literature Reviewmentioning

confidence: 99%

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

Mohammed

Abdulla²

2020

UHD J SCI TECH

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Digital image processing has a significant role in different research areas, including medical image processing, object detection, biometrics, information hiding, and image compression. Image segmentation, which is one of the most important steps in processing medical image, makes the objects inside images more meaningful. For example, from microscopic images, blood cancer can be identified which is known as leukemia; for this purpose at first, the white blood cells (WBCs) need to be segmented. This paper focuses on developing a segmentation technique for segmenting WBCs from microscopic blood images based on thresholding segmentation technique and it compares with the most commonly used segmentation technique which is known as color-k-means clustering. The comparison is done based on three well-known measurements, used for evaluating segmentation techniques which are probability random index, variance of information, and global consistency error. Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color-k-means which are 70.8144 ms and 204.7188 ms, respectively.

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Overlapping White Blood Cells Detection Based on Watershed Transform and Circle Fitting

Abstract: Abstract. White blood cells (WBCs

Cited by 6 publications

References 17 publications

Classification of WBC cell classification using fully connected convolution neural network

Classification of WBC cell classification using fully connected convolution neural network

Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images

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