Automatic atrium contour tracking in ultrasound imaging

Hsu, Wei‐Yen

doi:10.3233/ica-160517

Cited by 13 publications

(3 citation statements)

References 67 publications

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“…Over the last few decades, many biomedical signal processing techniques have been studied [1][2][3][4][5][6]. In particular, the field of biomedical signal recording has had a major impact in modern societies, as most medical care is provided based on this information, especially in relation to heart and lung diseases [6][7][8][9][10]. However, in many cases, obtaining such signals involves invasive or semi-invasive techniques and, in others, especially expensive and cumbersome setups.…”

Section: Introductionmentioning

confidence: 99%

A system for biomedical audio signal processing based on high performance computing techniques

Muñoz-Montoro¹,

Revuelta-Sanz²,

Villalón-Fernández³

et al. 2022

ICA

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In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a cost-effective microphone, multipurpose microcontroller and computing node that could be a mobile phone or general-purpose computer. Using parallel and high-performance techniques, this setup allows one to register and wirelessly multicast the recorded biomedical signals to computing nodes in real time. The developed prototype was used as a case study to estimate the heart rate (HR) from the captured biomedical audio signal. In this regard, the developed algorithm for estimating HR comprises three stages: preprocessing, separation, and HR estimation. In the first stage, the signal captured by the microphone is adapted for processing. Subsequently, a separation stage was proposed to alleviate the acoustic interference between the lungs and heart. The separation is performed by combining a non-negative matrix factorization algorithm, clustering approach, and soft-filter strategy. Finally, HR estimation was obtained using a novel and efficient method based on the autocorrelation function. The developed prototype could be used not only for the estimation of the HR, but also for the retrieval of other biomedical information related to the recording of cardiac or respiratory audio signals. The proposed method was evaluated using well-known datasets and compared with state-of-the-art algorithms for source-separation. The results showed that it is possible to obtain an accurate separation and reliable real-time estimation in terms of source separation metrics and relative error in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques. Finally, the proposed prototype was validated in a real-world scenario.

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Section: Introductionmentioning

confidence: 99%

A system for biomedical audio signal processing based on high performance computing techniques

Muñoz-Montoro¹,

Revuelta-Sanz²,

Villalón-Fernández³

et al. 2022

ICA

View full text Add to dashboard Cite

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“…[ 3 – 5 ] Cancer size is determined to be one of the predicting factor for surviving. [ 6 , 7 ] However, discrepancy between radiological and pathological size of kidney cancer is noted. [ 8 , 9 ] Kidney cancer, which is the third most common urologic tumor, [ 1 ] with roughly 20% to 30% of quiet.…”

Section: Introductionmentioning

confidence: 99%

Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model

Hsu

Hsu³

2020

Medicine

Self Cite

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In the present study, we retrospectively analyzed the records of surgical confirmed kidney cancer with renal cell carcinoma pathology in the database of the hospital. We evaluated the significance of cancer size by assessing the outcomes of proposed adaptive active contour model (ACM). The aim of our study was to develop an adaptive ACM method to measure the radiological size of kidney cancer on computed tomography in the hospital patients. This paper proposed a set of medical image processing, applying images provided by the hospital and select the more obvious cases by the doctors, after the first treatment to remove noise image, and the kidney cancer contour would be circled by using the proposed adaptive ACM method. The results showed that the experimental outcome has highly similarity with the medical professional manual contour. The accuracy rate is higher than 99%. We have developed a novel adaptive ACM approach that well combines a knowledge-based system to contour the kidney cancer size in computed tomography imaging to support the clinical decision.

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“…retina modelling [25] while others are related to the interpretation of what the cameras incorporated the eyes capture. Some examples include image segmentation [3], feature extraction [47,35], image recognition [7,26], and image processing in medical applications [16,17].…”

Section: Introductionmentioning

confidence: 99%

Geometry based three-dimensional image processing method for electronic cluster eye

Zhang

Zhu

et al. 2018

ICA

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In recent years, much attention has been paid to the electronic cluster eye (eCley), a new type of artificial compound eyes, because of its small size, wide field of view (FOV) and sensitivity to motion objects. An eCley is composed of a certain number of optical channels organized as an array. Each optical channel spans a small and fixed field of view (FOV). To obtain a complete image with a full FOV, the images from all the optical channels are required to be fused together. The parallax from unparallel neighboring optical channels in eCley may lead to reconstructed image blurring and incorrectly estimated depth. To solve this problem, this paper proposes a geometry based three-dimensional image processing method (G3D) for eCley to obtain a complete focused image and dense depth map. In G3D, we derive the geometry relationship of optical channels in eCley to obtain the mathematical relation between the parallax and depth among unparallel neighboring optical channels. Based on the geometry relationship, all of the optical channels are used to estimate the depth map and reconstruct a focused image. Subsequently, by using an edge-aware interpolation method, we can further gain a sharply focused image and a depth map. The effectiveness of the proposed method is verified by the experimental results.

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Automatic atrium contour tracking in ultrasound imaging

Cited by 13 publications

References 67 publications

A system for biomedical audio signal processing based on high performance computing techniques

A system for biomedical audio signal processing based on high performance computing techniques

Improving segmentation accuracy of CT kidney cancer images using adaptive active contour model

Geometry based three-dimensional image processing method for electronic cluster eye

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