A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

Anilkumar, K.; Manoj, Vasupalli; Sagi, Tomer

doi:10.1016/j.bbe.2020.08.010

Cited by 61 publications

(28 citation statements)

References 67 publications

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“…Automated methods for leukemia diagnosing using microscopic blood smear images increased since last decade and the increase in their number can be clearly observed. Many studies have proposed automated detection of acute type of leukemia (ALL and AML) but only a few studies have proposed framework for the diagnosis of chronic type of leukemia (CML and CLL) using image processing methods [14]. Although numerous studies have been proposed for the automated diagnosis of acute lymphoblastic leukemia, only fewer studies have proposed the methods to diagnose the subtypes of ALL.…”

Section: Discussionmentioning

confidence: 99%

“…It is characterized by the formation of immature blast cells which are other than lymphocytes [13]. In AML, the bone marrow may also make abnormal red blood cells and platelets [14]. The symptoms of AML include anaemia, fever, ulcers of mucous membranes and granulocytic insufficiency [15], [16].…”

Section: A Classes Of Leukemiamentioning

confidence: 99%

“…These categories further divided into famous techniques as cauterization, morphological filtering, contrast stretching, thresholding, color space conversion and watershed segmentation and various Deep learning networks [86], [87]. [14] has reviewed various methods of segmentation that have been used for segmenting bone marrow and peripheral blood smear images for classifying various types of leukemia. It is observed that segmentation of leucocytes is performed using clustering particularly K-means clustering and neural networks during the years 2016-2020.…”

Section: Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: A Classes Of Leukemiamentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

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Automated Diagnosis of Leukemia: A Comprehensive Review

Shah

Naqvi

Naveed³

et al. 2021

IEEE Access

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“…On the other hand, some other methods apply post-processing to overcome the problems arising from over-segmentation. Overall, segmentation methods can be grouped into thresholding, boundary detection, and region growing [ 27 , 29 , 31 , 36 , 37 ]. Those methods vary in the way that the image features are treated and the way the appearance and shape of the target are modeled [ 38 ].…”

Section: An Overview Of Image Analysis Methods For Microfluidic Blmentioning

confidence: 99%

Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels

et al. 2021

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In blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analyzed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well known microfluidic phenomena cell-free layer, two developed methods are presented and discussed in order to demonstrate their feasibility to obtain accurate data acquisition in such studies. Additionally, a comparison analysis between manual and automatic methods was performed.

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“…Coefficient of correlation helps in judging resemblances between two measured vector quantities, while analyzing whether the two quantities are identical or completely different. Pearson's correlation coefficient is denoted as r and is used in shape learning and computer image identification (Kaur et al, 2012;Anilkumar et al, 2020) (Anilkumar et al, 2020).…”

Section: Similarity Of Regionsmentioning

confidence: 99%

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021

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Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

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A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia

Cited by 61 publications

References 67 publications

Automated Diagnosis of Leukemia: A Comprehensive Review

Automated Diagnosis of Leukemia: A Comprehensive Review

Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

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