Automatic left ventricular feature extraction and visualisation from echocardiographic images

Setarehdan, Seyed Kamaledin; Soraghan, John J.

doi:10.1109/cic.1996.542460

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…(3) Lastly, the user interface problem existed, i.e., it required user interaction at the first stage. One solution would be to use Setarehdan and Soraghan's [107] approach of estimating a center point in the LV using fuzzy logic and then computing the raw edges using the wavelet transform. This raw edge boundary can now undergo the regularization term as used by Chalana et al in the boundary estimation process of a fetal head in ultrasound images.…”

Section: Limitations Of Chalana's Methodmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Algorithmic Approaches to Medical Image Segmentation

Suri¹,

Setarehdan²,

Singh³

2002

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Section: Limitations Of Chalana's Methodmentioning

confidence: 99%

“…Setarehdan and Soraghan [107], [189], [190] addressed the issue of LV boundary extraction in echocardiogram images using a combination of fuzzy logic (see Zadeh [191]) and multi-resolution edge detection. Setarehdan and Soraghan's algorithm consisted of two major steps.…”

Section: Wavelet-based LV Edge Detectionmentioning

confidence: 99%

Advanced Algorithmic Approaches to Medical Image Segmentation

Suri¹,

Setarehdan²,

Singh³

2002

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“…Edge detection has also been effected by means of stack filter^,'^ velocity maps,24 optimal Deriche filtering? and other methods.26' 27 Edge detection can also be accomplished by regional segmentation, in which the image is first segmented into dissimilar regions, such as blood and myocardium; edge points are then defined by the region boundaries. The most commonly used regional segmentation method is some form of thresholding pr~cedure.~.~.~.~.…”

Section: Previous Methodsmentioning

confidence: 99%

Edge Detection in Ultrasound Images Based on Differential Tissue Attenuation Rates

Hammoude¹

1999

Ultrason Imaging

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Identification of the cardiac borders is a basic goal in the analysis of echocardiographic images. This is a requirement for various analysis purposes, such as the derivation of quantitative parameters, for wall motion analysis and for the creation of three-dimensional reconstructions. Border identification is usually accomplished by first identifying a set of generic edge points, then extracting the border of interest from the edge map. A variety of edge detection algorithms have been reported in the literature. However, the most commonly used methods are all based on the same underlying tissue discriminant, namely, the widely differing backscatter levels of the different types of tissue that form the cardiac borders, in particular, blood and myocardium. We have implemented a new method of edge detection based on an entirely different ultrasonic tissue characteristic--that of attenuation, the rate at which the tissue absorbs ultrasonic energy. This characteristic also differs widely between blood and myocardium, and therefore provides an alternative means of discriminating between these two types of tissue. The method searches for the abrupt change in attenuation rate that characteristically occurs at the blood/myocardium interface. At each point in the image, the average attenuation rates of the portions of tissue radially preceding and radially following the point are determined by least-squares regression. If one of these attenuation rates is sufficiently close to that of blood, and the other is sufficiently close to that of myocardium, then the point is flagged as an edge point. This paper describes how this method is implemented in software, and presents the results of applying the method to a library of sample images.

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“…short-axis views of a sequence of images from one cardiac cycle was proposed by Setarehdan and Soraghan, (1996) in the first step for boundary extraction of the LV. In the work, the initial boundary points were defined based on the global maximum point of wavelet transformation to defined the candidate points of the cavity boundary, Upon defining the candidate points Cubic B-Spline was employed to generate the smooth LV boundary through these points.…”

Section: Jcsmentioning

confidence: 99%

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

Abboud¹,

Rahmat²,

Kadiman³

et al. 2014

Journal of Computer Science

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The complexity and criticality of heart disease diagnosis and treatment have frequently attracted the attention of researchers to develop approaches that can handle accurate clinical assessments. One of these complexities is measuring the Right Ventricle stroke volume. Therefore, different cardiac imaging techniques have been developed to help specialists in their regular clinical work and in the operating theatre to assist in the diagnosis, monitoring of the treatment and to ensure the success of a cardiac intervention as far as possible. Echocardiography is one of the cardiac imaging techniques and is considered as the most effective imaging technique as it is minimally invasive, quick and the cheapest method. Many studies have been conducted on Right Ventricle assessment from echocariographic images. This review is aimed at presenting the previous technical elements and algorithms used to measure the ventricle stroke volume from echocardiographic images. The review focuses on the five technical approaches that have been used, which are pre-processing, End-Distally (ED) and End-Systolic (ES) frame detection, cavity segmentation, the stoke volume measurement and 3D reconstruction. Finally, a summary will conclude this study.

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Automatic left ventricular feature extraction and visualisation from echocardiographic images

Cited by 12 publications

References 6 publications

Advanced Algorithmic Approaches to Medical Image Segmentation

Advanced Algorithmic Approaches to Medical Image Segmentation

Edge Detection in Ultrasound Images Based on Differential Tissue Attenuation Rates

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

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