Automatic detection of end systole and end diastole within a sequence of 2-D echocardiographic images using modified Isomap algorithm

Shalbaf, Ahmad; Behnam, Hamid; Gifani, Parisa; Alizadeh-Sani, Zahra

doi:10.1109/mecbme.2011.5752104

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…Machine learning approaches have also been applied to automatically detect ED and ES frames from 2D echocardiography images (B-mode). This includes manifold learning [9], speckle tracking [10], correlation-based frame-to-frame deviation measures [11,12], nonlinear filtering and boundary detection techniques [13].…”

Section: Value Of Independence From Ecgmentioning

confidence: 99%

Multibeat echocardiographic phase detection using deep neural networks

Lane

Azarmehr

Jevsikov

et al. 2021

Computers in Biology and Medicine

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Section: Value Of Independence From Ecgmentioning

confidence: 99%

Multibeat echocardiographic phase detection using deep neural networks

Lane

Azarmehr

Jevsikov

et al. 2021

Computers in Biology and Medicine

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“…Gifani et al (2010) presented an automatic detection method for ED and ES of LV from two and four chamber views using unsupervised learning algorithm Locally Linear Embedding (LLE) for three cardiac consecutive cycles. Shalbaf et al (2011) performedimage registration for echocardiographic images of 6 volunteers, distance computation and finally the classical Multi-Dimensional Scaling (MDS) used to construct low dimensional representation of 2D LV images to generate Iso-map. Then, they computed manifold model of seven phases of cardiac cycles to determine ED and ES stages automatically.…”

Section: End-diastolic and End-systolic Detectionmentioning

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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“…The authors of [6, 7] recognised the end‐systole and end‐diastole frames using the high‐dimensional data visualisation methods such as dimensionality reduction algorithms, which mapped the high dimensional of echocardiography image to low‐dimensional space. The authors of [8–10] carried out the further development of artificial neural networks and machine learning algorithms for determining cardiac phases automatically.…”

Section: Introductionmentioning

confidence: 99%