Automated detection of left ventricular borders from intracardiac ultrasound images

Li, Weidong; DeJong, S.C.; McKay, Charles R.; Sonka, Milan

doi:10.1109/cic.1996.542524

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…The table also shows border positioning errors previously achieved by using single-purpose semiautomated border detection methods with manually designed cost function. [39][40][41] Comparison of both methods shows that their accuracy are very well comparable. In several cases our method was able to identify the borders with even lower border positioning errors then the single-purpose application.…”

Section: Resultsmentioning

confidence: 82%

Medical image segmentation: Automated design of border detection criteria from examples

Brejl

Sonka

1999

J. Electron. Imaging

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This paper provides examples of several medical image analysis applications for which single-purpose border detection approaches were developed in the past. However, the utility of these and other existing automated and semiautomated medical image analysis systems is limited by their narrow, frequently singlepurpose orientation. After a general approach to graph-based optimal border detection is overviewed, a new method for design of image segmentation systems is reported, in which the criterion of optimality is automatically determined by learning from border tracing examples. Border features employed in the designed method are selected from a predefined global set using radial-basis neural networks. The method was validated in intracardiac, intravascular, and ovarian ultrasound images. The achieved performance was comparable to that of our previously reported single-purpose border detection methods. Our approach facilitates development of general multi-purpose image segmentation systems that can be trained for different types of image segmentation applications. © 1999 SPIE and IS&T. [S1017-9909(99)

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Section: Resultsmentioning

confidence: 82%

Medical image segmentation: Automated design of border detection criteria from examples

Brejl

Sonka

1999

J. Electron. Imaging

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“…Such border detection algorithm has been applied to a variety of ultrasound images [13][14][15]. The performance was shown to be comparable to human experts even in noisy situations.…”

Section: Border Detection Algorithmmentioning

confidence: 91%

<title>Automated analysis of brachial ultrasound time series</title>

Browning

Lauer

et al. 1998

SPIE Proceedings

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Atherosclerosis begins in childhood with the accumulation of lipid in the intima of arteries to form fatty streaks, advances through adult life when occlusive vascular disease may result in coronary heart disease, stroke and peripheral vascular disease. Non-invasive B-mode ultrasound has been found useful in studying risk factors in the symptom-free population. Large amount of data is acquired from continuous imaging of the vessels in a large study population. A high quality brachial vessel diameter measurement method is necessary such that accurate diameters can be measured consistently in all frames in a sequence, across different observers.Though human expert has the advantage over automated computer methods in recognizing noise during diameter measurement, manual measurement suffers from inter-and intra-observer variability. It is also time-consuming. An automated measurement method is presented in this paper which utilizes quality assurance approaches to adapt to specific image features, to recognize and minimize the noise effect. Experimental results showed the method's potential for clinical usage in the epidemiological studies.

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“…El segundo método más común para la extracción del borde es la minimización de una función de costo o energía [35,28,8,52,49,29,9,1]. En este método se busca el valor real de la función de costo en un dominio de configuraciones posibles del borde para un mapa de borde dado.…”

Section: Métodos De Funciones De Costounclassified

Selección de características de textura para la segmentación de cavidades cardiacas en imágenes ecocardiográficas

García

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The quality of the information contents in echocardiographic images is often reduced by the presence of dropout, speckle, movement artifact, and far field attenuation, although ultrasound is suitable to assessing the dynamic aspect of heart.The aim of this work is to find a set of texture features that optimally characterize the cardiac chambers from echocardiographic images and to use the to segment the image. In this work, seventy-seven texture characteristics from echographic and borders map images were extracted. An optimal subset of them was selected by an automatic process based on a separateness criterion, classification rate criterion and sequential forward algorithm. As a result the optimal set of texture characteristics found was: {Echo, Homogeneity of the co-ocurrence matrix at 90°, Central moment 22 of original image and Central moment 22 of borders map}. The classification rate reached was of 76.4%.

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Automated detection of left ventricular borders from intracardiac ultrasound images

Cited by 7 publications

References 9 publications

Medical image segmentation: Automated design of border detection criteria from examples

Medical image segmentation: Automated design of border detection criteria from examples

<title>Automated analysis of brachial ultrasound time series</title>

Selección de características de textura para la segmentación de cavidades cardiacas en imágenes ecocardiográficas

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