Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm

Ghane, Narjes; Vard, Alireza; Talebi, Ardeshir; Nematollahy, Pardis

doi:10.4103/2228-7477.205503

Cited by 73 publications

(12 citation statements)

References 14 publications

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“…Ghane et al [ 16 ] proposed a three-stage automated segmentation of cells. Color-based segmentation techniques such as CIE LAB and cyan, magenta, yellow, and black (CMYK) were applied, yielding similarity measures of 92% and 93% for the identification of leukocytes.…”

Section: Prior Artmentioning

confidence: 99%

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

Abrol

Dhalla

Gupta

et al. 2023

Wireless Pers Commun

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Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

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Section: Prior Artmentioning

confidence: 99%

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

Abrol

Dhalla

Gupta

et al. 2023

Wireless Pers Commun

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“…The proposed system used the custom pre-trained network which we explain in the next section. The extraction feature map pass through transform layer which responsible of organizing the feature map and creating YOLO v2 transform layer object to improves the network stability by determining location predictions [22], [38]. The YOLO v2 output layer is the last layer which responsible of determining and providing location of defined bound box for the target objects [39].…”

Section: Object Detectionmentioning

confidence: 99%

“…Recently, many researchers used several methods and techniques in medical aspect, especially images analysis, Their aims were extract the main feature of images which was used in automatic diagnosing of diseases. Ghane et al [38] the authors used segmentation technique as a main step by applying combination of modified watershed algorithms, thresholding and kmeans clustering. The proposed system consisted of three main stages; segmentation of WBCs, extraction of nuclei and separation of overlapping Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  cells and nuclei.…”

Section: Object Detectionmentioning

confidence: 99%

A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

Abas

Abdulazeez

Zeebaree

2022

IJEECS

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The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).

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“…K-means clustering was another famous segmentation approach. [13][14][15][16]20,32 applied K-means clustering-based segmentation on the G component of RGB image for two datasets and gained 99.51% and 99.74% accuracy respectively; when applied to a CMYK image, 98.89% accuracy was obtained. 16 Deep learning (DL) performed object class prediction by recognizing and learning patterns in visual inputs, making it the state-of-the-art method today.…”

Section: Literature Reviewmentioning

confidence: 99%

“…H component is extracted from the transformed HSV formatted microscopic image (Figure 2 and Figure 3). Subsequently, Gaussian filtering and Otsu thresholding 31,11,32 are applied three times to remove RBCs, RBCs boundaries, and to segment the WBCs. Results for each round can be viewed in Figure 4.…”

Section: Cell Segmentationmentioning

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 of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm

Cited by 73 publications

References 14 publications

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

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

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