An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

Vard, Alireza; Jamshidi, Kamal; Movahhedinia, Naser

doi:10.1007/s13246-012-0131-7

Cited by 21 publications

(3 citation statements)

References 32 publications

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“…The energy function for border coupled with thermal edge detection is as below. Overall, we set it based on NASTA and inspired by the pressure active contour model (Vard et al, 2012[33][34]):…”

Section: Methodsmentioning

confidence: 99%

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Zadeh

Haddadnia

Seryasat

et al. 2016

EXCLI Journal; 15:Doc532; ISSN 1611-2156

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Section: Methodsmentioning

confidence: 99%

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Zadeh

Haddadnia

Seryasat

et al. 2016

EXCLI Journal; 15:Doc532; ISSN 1611-2156

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“…A parametric active contour model for the segmentation of lumen and media-adventitia contours in IVUS images of coronary arteries was proposed by Vard et al (2012) . For the lumen contour segmentation, a method based on the short-term autocorrelation (STA) is used to remove speckle noise from the lumen region.…”

Section: Previous Studiesmentioning

confidence: 99%

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Jodas

Pereira

Tavares

2017

Medical Image Analysis

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Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.

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“…Ultrasound images feature gray-scale and intensity information, which are always used to diagnose disease 6 . To segment fetal skulls, the algorithms of fetal segmentations use these features together with boundary fragment models, active contouring, 7 and intensity-based features 8 . Machine learning techniques are potential schemes that can be used in ultrasound image segmentation.…”

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confidence: 99%

Automated Segmentation of Fetal Ultrasound Images Using Feature Attention Supervised Network

Liu

Zhao

2021

Ultrasound Quarterly

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Segmentation of anatomical structures from ultrasound images requires the expertise of an experienced clinician, but developing a machine automated segmentation process is complicated because of the existence of characteristic artifacts. In this article, we present a novel end-to-end network that enables automated measurements of the fetal head circumference (HC) and fetal abdomen circumference (AC) to be made from 2-dimensional (2D) ultrasound images during each pregnancy trimester. These measurements are necessary, because the HC and AC are used to predict gestational age and to monitor fetal growth. Automated HC and AC assessments are valuable for providing independent and objective results and are particularly useful for application in developing countries where trained sonographers are in short supply. We propose a scale attention expanding network that builds a feature pyramid inside the network, and the intermediate result of each scale is then concatenated to the feature with a fusion scheme for the next layer. Furthermore, a scale attention module is proposed for selecting the most useful scale and for reducing scale noise. To optimize the network, a deep supervision method based on boundary attention is employed. Results of experiments show that the scale attention expanding network obtained an absolute difference, Hausdorff distance, and dice similarity coefficient of 1.81 ± 1.69%, 1.22 ± 0.77%, and 97.94%, respectively, which were top results in the HC18 data set, and respective results on the abdomen set were 2.23 ± 2.38%, 0.42 ± 0.56%, and 98.04%. The experiments conducted demonstrate that our method provides a superior performance to existing fetal ultrasound segmentation methods.

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An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

Cited by 21 publications

References 32 publications

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Segmenting breast cancerous regions in thermal images using fuzzy active contours

Automatic segmentation of the lumen region in intravascular images of the coronary artery

Automated Segmentation of Fetal Ultrasound Images Using Feature Attention Supervised Network

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