Accurate Microscopic Red Blood Cell Image Enhancement and Segmentation

Shirazi, Syed Hamad; Umar, Arif Iqbal; Haq, Nuhman ul; Naz, Saeeda; Razzak, Muhammad Imran

doi:10.1007/978-3-319-16483-0_18

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The proposed method may perform negatively on the poor image quality. Shirazi et al (2016a) proposed a novel technique to enhance and segment leukocytes from microscopic images. Curvelet, transform, and wiener filters were used for image enhancement and noise removal.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They merged different methodologies like feature, color domain, and classifier for cell segmentation. In Imran & Ahmad (2017) , Shirazi et al (2016a) devised a statistical thresholding approach for segmenting red blood cells (RBCs), which was then succeeded by using Fuzzy C-means for accurate segmentation and boundary detection. Texture and geometrical features were used for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

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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%

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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“…In recent years, various studies have proposed computer vision, machine learning (ML) and deep learning (DL) frameworks that effectively classify the leukemia blood cells with better accuracy. One of the biggest advantages of computer vision-based leukemia detection methods is the ease in the diagnosis: instead of analysis of blood and bone marrow smear, the image of these samples can be remotely transported [23]. These automated methods of leukemia detection using microscopic blood smear images have utilized variety of methods that are efficient and effective.…”

Section: ) Progressmentioning

confidence: 99%

Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.INDEX TERMS Acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), computer-aided diagnostic (CAD), Leukemia.

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“…Investigations of cellular dynamics of normal and diseased cells require cell classification. Some cell classification methods are blood image segmentation, blood smear segmentation using deep learning, 1 overview of medical image and deep learning, 2 leukocyte segmentation, 3 and red blood image segmentation 4 . The Watershed algorithm 5 is a growing method which is often used for fluorescence microscopy images and cell segmentation 6‐10 .…”

Section: Introductionmentioning

confidence: 99%

“…Up till now, no reliable study is available to classify diseased and normal cells. Only a few studies have classified cells without differentiating diseased and normal cells, 1‐4,27‐29 normal cell and drug treated cell, 28,29 mitochondrial cell movement, 27 and blood image segmentation 1‐4 . In this study, we described methods that differ diseased cells from ordinary cells and thus, it becomes a specific contribution in the efficient classification of diseases and normal cells.…”

Section: Introductionmentioning

confidence: 99%

Deep learning recognition of diseased and normal cell representation

Iqbal

Ahmad

Luo

et al. 2020

Trans Emerging Tel Tech

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Cell classification refers to detecting normal and diseased cells from small amount of data. Sometimes, classification of cells becomes difficult because some cells fall into more than one categories/classes. Current state‐of‐the‐art cell classification methods have been developed on the bases of tumor cell classification but these methods cannot classify diseased or normal cells. This study investigated the performance of two classification methods traditional machine learning and deep learning (normal and diseased cell classification) to categorize normal and diseased cells. Millions of normal cells undergo controlled growth and uncontrolled growth may be involved in disease causation but their clinical applications remain limited due to difficulties in distinguishing normal and diseased cells. Previous studies are limited to identify, systematically, the normal and diseased cells. This study collected information about diseased or normal cells to check the networks for correct cell detection and then eliminated false‐positive cells. We used machine learning methods along with logistic regression, support vector machine, and CNN (convolutional neural network). We found that our proposed method classified better the normal and diseased cells. With the help of two types of images: normal and diseased cells, we trained a CNN that identified diseased cells with 98% accuracy and enabled the discovery of normal and diseased cells. As a result, it will advance the clinical utility of human diseased cells.

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Accurate Microscopic Red Blood Cell Image Enhancement and Segmentation

Cited by 11 publications

References 14 publications

Blood cell image segmentation and classification: a systematic review

Blood cell image segmentation and classification: a systematic review

Automated Diagnosis of Leukemia: A Comprehensive Review

Deep learning recognition of diseased and normal cell representation

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