A new algorithm of image segmentation using curve fitting based higher order polynomial smoothing

Biswas, Soumen; Ghoshal, Dibyendu; Hazra, Ranjay

doi:10.1016/j.ijleo.2016.06.110

Cited by 24 publications

(14 citation statements)

References 12 publications

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“…The rationale and method for calculating the relative low signal volume is extensively described in the supplemental Methods section (Appendix E1 [online]). Briefly, after filtering of images obtained at MRI to enhance contours, a segmentation of the lung volume was automatically obtained after calculation of a soft-tissue threshold from the histogram of signal intensity distribution by using the curve-fitting method (25,26). Second, voxel signal intensities contained within the lung volume were normalized between 0 and 1, with native signal intensity from lung air…”

Section: Validation Of Mri In the Assessment Of Emphysema Severitymentioning

confidence: 99%

Automated Volumetric Quantification of Emphysema Severity by Using Ultrashort Echo Time MRI: Validation in Participants with Chronic Obstructive Pulmonary Disease

Benlala¹,

Berger²,

Girodet³

et al. 2019

Radiology

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hronic obstructive pulmonary disease (COPD) is characterized by persistent respiratory symptoms and airflow limitation (1) and is the third leading cause of mortality worldwide (2). In the past decades, chest CT has helped to provide valuable insights into COPD. Indeed, CT enables noninvasive, in vivo, and three-dimensional (3D) quantification of the key components of the disease, such as bronchial remodeling (3) and emphysema (4). Evidence of COPD subtypes (1), relationship with genetic variants (5), longitudinal follow-up (6), and mortality (7) have been investigated at CT. More recently, in an era where the advent of artificial intelligence may allow intricate combinations of both morphologic and functional information, lung MRI has emerged as a radiation-free modality for the longitudinal follow-up of lung diseases and (8) to help better identify participant phenotype or predict disease outcomes (9). Studies related to ventilation (10), perfusion (11), dynamic biomarkers (12), and coupling between the cardiovascular and pulmonary systems (13,14) at MRI have been performed. However, there are few studies that evaluated the regional extent of emphysema by using MRI (15-18). Recent advances with ultrashort echo times (UTEs) have been shown to enable submillimeter resolution with good contrast in three dimensions (19,20).

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Section: Validation Of Mri In the Assessment Of Emphysema Severitymentioning

confidence: 99%

Automated Volumetric Quantification of Emphysema Severity by Using Ultrashort Echo Time MRI: Validation in Participants with Chronic Obstructive Pulmonary Disease

Benlala¹,

Berger²,

Girodet³

et al. 2019

Radiology

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“…It was subject to Gaussian distribution approximately [21] ( Figure 1c). And appropriate segmentation threshold could be used to find the lost foreground pixels based on Gaussian curve fitting.…”

Section: Gaussian Curve Fittingmentioning

confidence: 99%

Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

Song

2018

International Journal of Agricultural and Biological Engineering

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Green apple targets are difficult to identify for having similar color with backgrounds such as leaves. The primary goal of this study was to detect green apples in natural scenes by applying saliency detection and Gaussian curve fitting algorithm. Firstly, the image was represented as a close-loop graph with superpixels as nodes. These nodes were ranked based on the similarity to background and foreground queries to generate the final saliency map. Secondly, Gaussian curve fitting was carried out to fit the V-component in YUV color space in salient areas, and a threshold was selected to binarize the image. To verify the validity of the proposed algorithm, 55 images were selected and compared with the common used image segmentation algorithms such as k-means clustering algorithm and FCM (Fuzzy C-means clustering algorithm). Four parameters including recognition ratio, FPR (false positive rate), FNR (false negative rate) and FDR (false detection rate) were used to evaluate the results, which were 91.84%, 1.36%, 8.16% and 4.22%, respectively. The results indicated that it was effective and feasible to apply this method to the detection of green apples in nature scenes. Citation: Li B R, Long Y, Song H B. Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting. Int J Agric & Biol Eng, 2018; 11(1): 192-198. Li B R, et al. Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting Vol.

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“…To compare the results of the two algorithms more objectively, a universal image quality index (UIQI) is adopted, which is independent of the contents and types of the image under test [19,20].…”

Section: Comparison Against Gaussian Filtering and Canny Edge Extractiomentioning

confidence: 99%

A Generic Adaptive Fractal Filtering Algorithm for Identifying Work Piece Defects on Multiple Surfaces

Gong¹,

Lin²,

Z³

et al. 2018

Preprint

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In addition to image filtering in the spatial and frequency domains, fractal characteristics induced algorithms offers considerable flexibility in the design and implementations of image processing solutions in areas such as image enhancement, image restoration, image data compression and spectrum of applications of practical interests. Facing up to a real-world problem of identifying workpiece surface defects, a generic adaptive fractal filtering algorithm is proposed, which shows advantages on the problems of target recognition, feature extraction and image denoising at multiple scales. First, we reveal the physical principles underlying between signal SNR and its representative fractal dimension indicative parameters, validating that the fractal dimension can be used to adaptively obtain the image features. Second, an adaptive fractal filtering algorithm (Abbreviated as AFFA) is proposed according to the identified correlation between the image fractal dimensions and the scales of objects, and it is verified by a benchmarking image processing case study. Third, by using the proposed fractal filtering algorithm, surface defects on a flange workpiece are identified. Compared to conventional image processing algorithms, the proposed algorithm shows superior computing simplicity and better performance Numerical analysis and engineering case studies show that the fractal dimension is eligible for deriving an adaptive filtering algorithm for diverse-scale object identification, and the proposed AFFA is feasible for general application in workpiece surface defect detection. 

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A new algorithm of image segmentation using curve fitting based higher order polynomial smoothing

Cited by 24 publications

References 12 publications

Automated Volumetric Quantification of Emphysema Severity by Using Ultrashort Echo Time MRI: Validation in Participants with Chronic Obstructive Pulmonary Disease

Automated Volumetric Quantification of Emphysema Severity by Using Ultrashort Echo Time MRI: Validation in Participants with Chronic Obstructive Pulmonary Disease

Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

A Generic Adaptive Fractal Filtering Algorithm for Identifying Work Piece Defects on Multiple Surfaces

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