A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia

Su, Jie; Liu, Shuai; Song, Jinming

doi:10.1016/j.cmpb.2017.09.011

Cited by 46 publications

(24 citation statements)

References 24 publications

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“…Segmentation is a method for image preprocessing applied for feature extraction and selection and could be considered as the first stage of feature extraction. Segmentation with the goal of extracting a cell from context [38][39][40]. Two techniques of blood smear image segmentation are more prominent and have received more attention from researchers.…”

Section: Segmentation In Pbs Imagesmentioning

confidence: 99%

“…Different machine learning algorithms have been used in most segmentation techniques. e purpose of cell segmentation is to identify the boundary between the nucleus and the cytoplasm for further characterization, such as the characterization of the nuclear properties, the properties of the cytoplasm, and the nuclear-to-cytoplasmic ratio, which is useful for explosive identification[39,46,47]. Many segmentation algorithms have been presented in the literature and the traditional ML algorithms based on selected features were the main and popular algorithms.Machine learning algorithms are used in the computational core of two categories of segmentation types.…”

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confidence: 99%

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Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Ghaderzadeh

Asadi

Hosseini

et al. 2021

Scientific Programming

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Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

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Section: Segmentation In Pbs Imagesmentioning

confidence: 99%

mentioning

confidence: 99%

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Ghaderzadeh

Asadi

Hosseini

et al. 2021

Scientific Programming

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“…30 Especially in leukemia, precise recognition of white blood cells with various segmentation techniques (filtering, enhancement, edge detection, feature extraction, and classification) 31 is crucial for correctly distinguishing between leukemic and non-leukemic cells. [32][33][34] ML can use these techniques to analyze whole slides with automated focusing. 35 Classification of leukemia subtypes (AML, acute lymphoblastic leukemia, chronic myeloid leukemia, and chronic lymphocytic leukemia) can be achieved by a variety of ML approaches such as DNN, 36 SVM, and k-means-clustering (an unsupervised ML technique in which similar data points are grouped into k clusters according to their distance to a cluster mean).…”

Section: Diagnosismentioning

confidence: 99%

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

Bornhäuser

Wendt²,

Middeke

2020

Blood Advances

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Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.

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“…Su [19] developed two stages of segmentation process using k-means clustering and HMRF (hidden Markov random field), which are used to group the six different types of AML cells from the bone marrow images. The segmentation algorithm achieved an accuracy of 96% to 98% (average) when compared with other existing segmentation methods.…”

Section: Related Literaturementioning

confidence: 99%

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

Inbarani

Azar

Ganesan³

2020

Electronics

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Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.

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A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia

Cited by 46 publications

References 24 publications

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

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