Research of the Leukocytes Segmentation Method in the Blood Cells Recognition Systems

Nikitaev, V. G.; Nagornov, O. V.; Pronichev, A. N.; Polyakov, E. V.; Zaytsev, Semen; Zakharenko, Y V; Dmitrieva, V. V.

doi:10.18502/ken.v3i2.1835

Cited by 4 publications

(4 citation statements)

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“…These analyses provide valuable information during morphological analysis and disease diagnostic procedures ( Liu & Long, 2019 ). In Nikitaev et al (2018) introduced a technique for segmenting leukocytes from blood and bone marrow cells. A total of 1,018 cell images were formed out of 50 samples.…”

Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

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Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

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Section: Literature Reviewmentioning

confidence: 99%

Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Previous work on blood cells like white blood cells (WBCs) and red blood cells (RBCs) [4][5][6][7] has used K-means, Zack algorithm, gradient magnitude, watershed transform, and SVM for segmentation along with some preprocessing for image enhancement [13]. ese works show outstanding performance for efficient detection and segmentation of blood cells.…”

Section: Related Workmentioning

confidence: 99%

“…In Figure 13, the chart shows the number of each blood cell pixel in the test image. [19] only targets the RBC segmentation with 93.5% accuracy, while the techniques in [4,15,29] target only WBC segmentation with an accuracy rate of 82%, 97.6%, and 90%, respectively. In [5], the author targets RBCs and WBCs with an accuracy rate of 94.8% and 97.2%, respectively.…”

Section: Classwise Pixel Counting Of Blood Cellsmentioning

confidence: 99%

“…Currently, several color space techniques are used for the partitioning of microscopic images like RGB, HIS, and LAB, but RGB is the most commonly used color space technique due to its close association with the human visual system [3]. Most of the previous works target the single cell segmentation [4][5][6][7]. e single cell technique targets only one type of cell (WBCs or RBCs) for segmentation at one time.…”

Section: Introductionmentioning

confidence: 99%

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Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images

Shahzad

Umar

Khan

et al. 2020

Computational and Mathematical Methods in Medicine

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Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. e proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.

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