Attenuation Correction and Normalisation for Quantification of Contrast Enhancement in Ultrasound Images of Carotid Arteries

The geometry of the imaged transverse cross-section of carotid arteries in in-vivo B-mode ultrasound images are most times irregular, unsymmetrical, full of speckles and usually non-uniform. We had earlier developed a technique of cardinal point symmetry landmark distribution model (CPS-LDM) to completely characterize the Region of Interest (ROI) of the geometric shape of thick-walled simulated B-mode ultrasound images of carotid artery imaged in the transverse plane, but this was based on the symmetric property of the image. In this paper, this developed technique was applied to completely characterize the region of interest of the geometric shape of in-vivo B-mode ultrasound images of non-uniform carotid artery imaged in the transverse plane. In order to adapt the CPS-LD Model to the in-vivo carotid artery images, the single VS-VS vertical symmetry line common to the four ROIs of the symmetric image is replaced with each ROI having its own VS-VS vertical symmetry line. This adjustment enables the in-vivo carotid artery images possess symmetric properties, hence, ensuring that all mathematical operations of the CPS-LD Model are conveniently applied to them. This adaptability was observed to work well in segmenting the in-vivo carotid artery images. This paper shows the adaptive ability of the developed CPS-LD Model to successfully annotate and segment in-vivo B-mode ultrasound images of carotid arteries in the transverse cross-sectional plane either they are symmetrical or unsymmetrical.

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Accuracy of a semi-automatic technique for segmentation of the carotid arteries from 3D ultrasound images

Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. No.99CH37015) ◽

10.1109/iembs.1999.804312 ◽

2003 ◽

Author(s):

J.D. Gill ◽

H. Ladak ◽

D.A. Steinman ◽

A. Fenster

Keyword(s):

Carotid Arteries ◽

3D Ultrasound ◽

Ultrasound Images ◽

Automatic Technique

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A Multiscale Morphological Approach to Local Contrast Enhancement for Ultrasound Images

2010 International Conference on Computational and Information Sciences ◽

10.1109/iccis.2010.282 ◽

2010 ◽

Cited By ~ 4

Author(s):

Bo Peng ◽

Yang Wang ◽

Xianfeng Yang

Keyword(s):

Contrast Enhancement ◽

Ultrasound Images ◽

Local Contrast ◽

Morphological Approach ◽

Local Contrast Enhancement

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Automatic Localization of the Carotid Wall in Ultrasound Imaging Based on Convolutional Neural Network

10.5753/wvc.2019.7635 ◽

2019 ◽

Author(s):

Alexandre G. Silva ◽

Eryk K. Da Cruz ◽

Rangel Arthur ◽

Giulliano P. Carnielli ◽

Henri A. De Godoy ◽

...

Keyword(s):

Neural Network ◽

Carotid Arteries ◽

Intima Media Thickness ◽

Inflammatory Cells ◽

Carotid Intima Media Thickness ◽

Ultrasound Images ◽

Non Invasive ◽

The World ◽

Carotid Wall ◽

Automatic Localization

Atherosclerosis is the leading cause of death in the world. It is a cardiovascular disease characterized by the accumulation of inﬂammatory cells and lipids inside the artery walls. In Brazil, more than 30% of all deaths are due to cardiovascular diseases. The carotid intima-media thickness, obtained from ultrasound images, maybe an early estimate of atherosclerosis.This test is fast, safe and non-invasive, as well as being reproducible and relatively inexpensive. In this context, this work, based on convolutional neural networks and techniques of mathematical morphology, consists in automatically locating the region that covers the intima and media sublayers of carotid arteries. The proposed method obtained a score of 88% considering the trained model applied to 234 ultrasonographic images in two different datasets. The analysis of the neighborhood of the points obtained can be useful in the evaluation of cardiovascular risk factors.

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Segmentation of the Carotid Arteries from 3D Ultrasound Images

Advanced Computational Approaches to Biomedical Engineering ◽

10.1007/978-3-642-41539-5_6 ◽

2013 ◽

pp. 131-157

Author(s):

Eranga Ukwatta ◽

Aaron Fenster

Keyword(s):

Carotid Arteries ◽

3D Ultrasound ◽

Ultrasound Images

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Improved Adaptive Wavelet Thresholding for Effective Speckle Noise Reduction in Low Contrast Medical Images

Journal of Circuits System and Computers ◽

10.1142/s0218126619501767 ◽

2019 ◽

Vol 28 (10) ◽

pp. 1950176 ◽

Cited By ~ 2

Author(s):

P. Sreelatha ◽

M. Ezhilarasi

Keyword(s):

Contrast Enhancement ◽

Speckle Noise ◽

Noise Removal ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Gamma Correction ◽

Significant Information ◽

Adaptive Wavelet ◽

Low Contrast ◽

Image Contrast Enhancement

Informative images endure from poor contrast and noise during image acquisition. Significant information retrieval necessitates image contrast enhancement and removal of noise as a prerequisite before any further processing can be done. Dominant applications with low contrast images affected by speckle noise are medical ultrasound images. The objective of this work is to improve the effectiveness of the preprocessing stage in medical ultrasound images by enhancing the image while retaining its structural characteristics. For image enhancement, this work proposes to develop an automatic contrast enhancement technique using cumulative histogram equalization and gamma correction based on the image. For noise removal, this work proposes an algorithm Gamma Correction with Exponentially Adaptive Threshold (GCEAT) which suggests the use of GC for contrast enhancement along with a new wavelet-based adaptive soft thresholding technique for noise removal. The proposed GCEAT-based image de-noising is validated with other enhancement and noise removal techniques. Experimental results with low contrast synthetic and actual ultrasound images show that the suggested proposed system performs better than existing contrast enhancement techniques. Encouraging results were obtained with medical ultrasound images in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM) and Average Intensity (AI).

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