White Blood Cell Detection and Segmentation from Fluorescent Images with an Improved Algorithm using K-means Clustering and Morphological Operators

Ferdosi, Bilkis Jamal; Nowshin, Sharmilee; Sabera, Farzana Ahmed; Habiba,

doi:10.1109/ceeict.2018.8628068

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…Working with a ground truth of 250 images (720 × 576 px), Ferdosi et al utilized 𝑘-means clustering and morphological operators to segment WBCs from fluorescence microscopy images. Best classification results were achieved for the WBC class of Basophils with a precision of 98.36 % and a sensitivity of 96.49 % (Ferdosi et al, 2018). Similar results were presented by Zhang et al who achieved an accuracy of 98 % and 92 % for segmentation and classification, respectively.…”

Section: Discussionsupporting

confidence: 81%

Automation of hemocompatibility analysis using image segmentation and supervised classification

Clauser

Maas

Schmitz‐Rode

et al. 2021

Engineering Applications of Artificial Intelligence

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Section: Discussionsupporting

confidence: 81%

Automation of hemocompatibility analysis using image segmentation and supervised classification

Clauser

Maas

Schmitz‐Rode

et al. 2021

Engineering Applications of Artificial Intelligence

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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%

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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“…For peripheral blood cell microscopic image analysis, several studies were devoted to developing a segmentation method for each type of the WBCs, as each type has its unique characteristics, for assisting in a better counting process [14], [16]- [18]. Clustering methods were designed to recognize the cluster that contains the WBCs using different techniques, such as K-means clustering with morphological operators [19], fuzzy C-means clustering [20], and meanshift clustering to generate island-clustering texture method [21]. On the other hand, the active contour models were employed for segmenting overlapped WBCs [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

2021

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Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.

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White Blood Cell Detection and Segmentation from Fluorescent Images with an Improved Algorithm using K-means Clustering and Morphological Operators

Cited by 15 publications

References 14 publications

Automation of hemocompatibility analysis using image segmentation and supervised classification

Automation of hemocompatibility analysis using image segmentation and supervised classification

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

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

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