Image segmentation based on gray level and local relative entropy two dimensional histogram

Yang, Wei; Cai, Lulu; Wu, Fei

doi:10.1371/journal.pone.0229651

Cited by 34 publications

(16 citation statements)

References 25 publications

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“…The normalized histogram counts are represented by p, and entropy is defined assum(p.*log2(p)). Entropy based filtering finds application in segmenting images of differentbackground [8].…”

Section: Local Entropy Filtermentioning

confidence: 99%

Comparative analysis of elementary and edge-preserving spatial filters in noise removal – a comprehensive study

Sivakumar¹,

Sakthiraam.²,

Snehal.³

et al. 2021

JUSST

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Preserving the edges and information is one of the main purposes of edge-preserving filters. That is, they’re employed to smooth a picture, and minimize halos, phantoms, and edge blur over the edge. They have a nonlinear relationship between one thing and another. Bilateral filters, anisotropic diffusion filters, directed filters, and trilateral filters are all types of example filters. The filter family may be used in a wide range of image processing tasks, such as denoising, video abstraction, demosaicing, optical flow estimation, stereo matching, tone mapping, style transfer, relighting, and others. The paper gives a clear description of edge-preserving filters, from the heat diffusion equation in ancient times to the present, explaining their numerous applications and detailing their numerous uses. Additionally, mathematical analysis is included, as well as efficient and optimized implementations. The focus is on preserving the boundaries, spikes, and canyons, and the information is given clearly and in detail. Finally, it offers a realistic representation of efficient implementation, as well as a comprehensive research scope for future hardware implementation.

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“…The normalized histogram counts are represented by p, and entropy is defined assum(p.*log2(p)). Entropy based filtering finds application in segmenting images of differentbackground [8].…”

Section: Local Entropy Filtermentioning

confidence: 99%

Comparative analysis of elementary and edge-preserving spatial filters in noise removal – a comprehensive study

Sivakumar¹,

Sakthiraam.²,

Snehal.³

et al. 2021

JUSST

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“…Entropy can be explained as the measure of uncertainty or randomness in an image [43]. The global entropy of an image can be mathematically calculated with (2).…”

Section: Local Entropy (Le)mentioning

confidence: 99%

A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods

Yaşar

Ceylan

2020

Multimed Tools Appl

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The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, 1.396 lung CT images in total (386 Covid-19 and 1.010 Non-Covid-19) were subjected to automatic classification. In this study, Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning. Within the scope of the study, a 23-layer CNN architecture was designed and used as a classifier. Also, training and testing processes were performed for Alexnet and Mobilenetv2 CNN architectures as well. The classification results were also calculated for the case of increasing the number of images used in training for the first 23-layer CNN architecture by 5, 10, and 20 times using data augmentation methods. To reveal the effect of the change in the number of images in the training and test clusters on the results, two different training and testing processes, 2-fold and 10-fold cross-validation, were performed and the results of the study were calculated. As a result, thanks to these detailed calculations performed within the scope of the study, a comprehensive comparison of the success of the texture analysis method, machine learning, and deep learning methods in Covid-19 classification from CT images was made. The highest mean sensitivity, specificity, accuracy, F-1 score, and AUC values obtained as a result of the study were 0

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“…The main advantage of VOLT is its local thresholding algorithm, which enables more flexible and stable results in comparison to global thresholding algorithms (such as Otsu's method). Inhomogeneous image intensities and local brightness variations are adequately compensated for [48,51]. The robustness and flexibility of VOLT to image artifacts can be further increased using different radius sizes.…”

Section: Volt-performance and Usabilitymentioning

confidence: 99%

VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI

et al. 2020

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Background Objective and volumetric quantification is a necessary step in the assessment and comparison of endolymphatic hydrops (ELH) results. Here, we introduce a novel tool for automatic volumetric segmentation of the endolymphatic space (ELS) for ELH detection in delayed intravenous gadolinium-enhanced magnetic resonance imaging of inner ear (iMRI) data. Methods The core component is a novel algorithm based on Volumetric Local Thresholding (VOLT). The study included three different data sets: a real-world data set (D1) to develop the novel ELH detection algorithm and two validating data sets, one artificial (D2) and one entirely unseen prospective real-world data set (D3). D1 included 210 inner ears of 105 patients (50 male; mean age 50.4 ± 17.1 years), and D3 included 20 inner ears of 10 patients (5 male; mean age 46.8 ± 14.4 years) with episodic vertigo attacks of different etiology. D1 and D3 did not differ significantly concerning age, gender, the grade of ELH, or data quality. As an artificial data set, D2 provided a known ground truth and consisted of an 8-bit cuboid volume using the same voxel-size and grid as real-world data with different sized cylindrical and cuboid-shaped cutouts (signal) whose grayscale values matched the real-world data set D1 (mean 68.7 ± 7.8; range 48.9-92.8). The evaluation included segmentation accuracy using the Sørensen-Dice overlap coefficient and segmentation precision by comparing the volume of the ELS. Results VOLT resulted in a high level of performance and accuracy in comparison with the respective gold standard. In the case of the artificial data set, VOLT outperformed the gold standard in higher noise levels. Data processing steps are fully automated and run without further user input in less than 60 s. ELS volume measured by automatic segmentation correlated significantly with the clinical grading of the ELS (p < 0.01). Conclusion VOLT enables an open-source reproducible, reliable, and automatic volumetric quantification of the inner ears' fluid space using MR volumetric assessment of endolymphatic hydrops. This tool constitutes an important step towards comparable and systematic big data analyses of the ELS in patients with the frequent syndrome of episodic vertigo attacks. A generic version of our three-dimensional thresholding algorithm has been made available to the scientific community via GitHub as an ImageJ-plugin.

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Image segmentation based on gray level and local relative entropy two dimensional histogram

Cited by 34 publications

References 25 publications

Comparative analysis of elementary and edge-preserving spatial filters in noise removal – a comprehensive study

Comparative analysis of elementary and edge-preserving spatial filters in noise removal – a comprehensive study

A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods

VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI

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