Application of Pattern Recognition Techniques for the Analysis of Histopathological Images

Krzyżak, Adam; Fevens, Thomas; Habibzadeh, Mehdi; Jeleń, Łukasz

doi:10.1007/978-3-642-20320-6_65

Cited by 7 publications

(4 citation statements)

References 57 publications

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“…Blood is a form consisting of plasma and blood cells in the heart's and veins' circulatory procedure, which we call the cardiovascular procedure in the body. Microscopic examination of peripheral blood smudge outcomes in hematology is expensive and time-consuming [1], [2], [3]. White blood cells (leukocyte, WBC) are usually misidentified because they are not naturally durable [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Segmentation White Blood Cells by Machine Learning Algorithms

Mollik¹

2023

World J. Bio. Pharm. Health Sci.

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Blood and its elements have a vital position in human life and are the best indicator for deciding many pathological states. Specifically, white blood cells are of great significance for diagnosing hematological disorders. In this analysis, 350 microscopic blood smudge images have experimented with six machine learning algorithms for the sort of white blood cells, and their renditions have resembled. Thirty-five distinct geometric and statistical (consistency) features have been pulled from blood pictures for practicum and test parameters of machine learning algorithms. According to the outcomes, the Multinomial Logistic Regression (MLR) algorithm accomplished better than the other techniques, with an average of 95% test victory. The MLR can be utilized for the automatic classification of white blood cells. It can be used mainly as a source for diagnosing diseases for hematologists and internal medicine experts.

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Section: Introductionmentioning

confidence: 99%

Segmentation White Blood Cells by Machine Learning Algorithms

Mollik¹

2023

World J. Bio. Pharm. Health Sci.

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“…Blood is a structure that consists of plasma and blood cells in the circulatory system of the heart and veins, which is called the cardiovascular system in the body. Microscopic analysis of marginal blood flow, resulting in hematology, is an expensive and inefficient procedure [1][2][3]. Blood is a vital body fluid and can be instrumental in identifying various pathological conditions.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Medical Imaging Approach for Various Blood Structure Classifications

Alruwaili

2021

Complexity

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Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis (SWLDA). SWLDA emphasizes on picking confined features from blood structure images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value (FL) and preselected or defaulting consequence levels such as F0 (i.e., if F0 > FL, the variable ZL is removed, and the F test is started again; otherwise, the regression equation is adopted). Finally, the system is trained by employing support vector machine (SVM) to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19.

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“…The size and shape of cardiomyocytes (CMs) are major determinants of the electrical propagation in the heart [ 1 , 2 , 3 , 4 ]. The cellular membrane acts as a capacitor and the capacitance correlates with the total surface of the cell.…”

Section: Introductionmentioning

confidence: 99%

Automatic Quantification of Cardiomyocyte Dimensions and Connexin 43 Lateralization in Fluorescence Images

et al. 2020

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Cardiomyocytes’ geometry and connexin 43 (CX43) amount and distribution are structural features that play a pivotal role in electrical conduction. Their quantitative assessment is of high interest in the study of arrhythmias, but it is usually hampered by the lack of automatic tools. In this work, we propose a software algorithm (Myocyte Automatic Retrieval and Tissue Analyzer, MARTA) to automatically detect myocytes from fluorescent microscopy images of cardiac tissue, measure their morphological features and evaluate the expression of CX43 and its degree of lateralization. The proposed software is based on the generation of cell masks, contouring of individual cells, enclosing of cells in minimum area rectangles and splitting of these rectangles into end-to-end and middle compartments to estimate CX43 lateral-to-total ratio. Application to human ventricular tissue images shows that mean differences between automatic and manual methods in terms of cardiomyocyte length and width are below 4 μm. The percentage of lateral CX43 also agrees between automatic and manual evaluation, with the interquartile range approximately covering from 3% to 30% in both cases. MARTA is not limited by fiber orientation and has an optimized speed by using contour filtering, which makes it run hundreds of times faster than a trained expert. Developed for CX43 studies in the left ventricle, MARTA is a flexible tool applicable to morphometric and lateralization studies of other markers in any heart chamber or even skeletal muscle. This open-access software is available online.

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Application of Pattern Recognition Techniques for the Analysis of Histopathological Images

Cited by 7 publications

References 57 publications

Segmentation White Blood Cells by Machine Learning Algorithms

Segmentation White Blood Cells by Machine Learning Algorithms

An Intelligent Medical Imaging Approach for Various Blood Structure Classifications

Automatic Quantification of Cardiomyocyte Dimensions and Connexin 43 Lateralization in Fluorescence Images

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