Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform

Vargas-Quintero, Lorena; Escalante-Ramrez, Boris; Marn, Lisbeth Camargo; Huerta, Mario Guzmn; Cosio, Fernando Armbula; Borboa-Olivares, Héctor

doi:10.1016/j.cmpb.2016.09.021

Cited by 29 publications

(7 citation statements)

References 36 publications

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“…Additionally, tracking both motion and structural data simultaneously can enhance fetal heart identification [94]. Multitexture AAM and Hermite transform (HT) [97] But the heart region was also located using optical flow [98]. [99] proposed an enhanced area-based Chan-Vese (RCV) model with energy minimization performed using the floral pollination method and the multilateral pollination-based CAT swarm optimization method.…”

Section: Nondeep Learning Approaches (Ndla)mentioning

confidence: 99%

Investigation on ultrasound images for detection of fetal congenital heart defects

Rufus

2024

Biomed. Phys. Eng. Express

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Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.

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Section: Nondeep Learning Approaches (Ndla)mentioning

confidence: 99%

Investigation on ultrasound images for detection of fetal congenital heart defects

Rufus

2024

Biomed. Phys. Eng. Express

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“…Step 1: 3D shape modeling using surface parameterization 4 forall voxels v in cuboid lattice l do 5 Step 2: Initialize self-similarity stochastic process Normalize voxel pairs distances ∆r;…”

Section: Classificationmentioning

confidence: 99%

“…Regarding ultrasound Bmode segmentation efforts, the endocardium and epicardium boundaries are delineated using a variety of strategies. In particular, statistical models which encode high-level knowledge, as the parametric or geometric deformable models, can handle topological changes robustly [4]- [7]. However, the boundary finding process requires a model to be initialized sufficiently close to the object to converge and is sometimes prone to local minima.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Segmentation in 3-D Echocardiographic Sequences Using Fractional Brownian Motion

Al-Kadi

2020

IEEE Trans. Biomed. Eng.

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An important aspect for an improved cardiac functional analysis is the accurate segmentation of the left ventricle (LV). A novel approach for fully-automated segmentation of the LV endocardium and epicardium contours is presented. This is mainly based on the natural physical characteristics of the LV shape structure. Both sides of the LV boundaries exhibit natural elliptical curvatures by having details on various scales, i.e. exhibiting fractal-like characteristics. The fractional Brownian motion (fBm), which is a non-stationary stochastic process, integrates well with the stochastic nature of ultrasound echoes. It has the advantage of representing a wide range of non-stationary signals and can quantify statistical local self-similarity throughout the time-sequence ultrasound images. The locally characterized boundaries of the fBm segmented LV were further iteratively refined using global information by means of second-order moments. The method is benchmarked using synthetic 3D+time echocardiographic sequences for normal and different ischemic cardiomyopathy, and results compared with state-of-the-art LV segmentation. Furthermore, the framework was validated against real data from canine cases with expert-defined segmentations and demonstrated improved accuracy. The fBm-based segmentation algorithm is fully automatic and has the potential to be used clinically together with 3D echocardiography for improved cardiovascular disease diagnosis.

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“…The Hermite transform appears when the polynomials used in the transform are Hermite polynomials [28]. This transform has applications in different fields, has been used for iris recognition [29], estimation in cardiac CT [30], and fetal echocardiography [31]. It has been applied to noise reduction and image fusion [32], [33], to make neural network correlation using simulated radar signals [34].…”

Section: Introductionmentioning

confidence: 99%

Image Correlation Using Fractional Hermite Transform

Castro-Valdez

Álvarez-Borrego

2020

IEEE Access

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In this paper, we generalize the Hermite transform into a fractional case using the fractional Fourier transform and the fractional convolution. The new methodology was evaluated using phytoplankton images with different illumination patterns and fragmented images. We found that the fractional Hermite transform had a better capability to recognize images. The discrimination coefficient was evaluated for the fractional Hermite transform and the conventional Hermite transform, finding more noise tolerate with the fractional Hermite transform. The Hermite fractional transform, in combination with the extreme phase filter, showed in a study, using fragmented diatom images, a better ability to classify diatoms, even when these had little information.

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Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform

Cited by 29 publications

References 36 publications

Investigation on ultrasound images for detection of fetal congenital heart defects

Investigation on ultrasound images for detection of fetal congenital heart defects

Spatio-Temporal Segmentation in 3-D Echocardiographic Sequences Using Fractional Brownian Motion

Image Correlation Using Fractional Hermite Transform

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