Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach

2018 ◽  
Vol 41 (2) ◽  
pp. 78-93 ◽  
Author(s):  
Yuan-yuan Wang ◽  
Chen-hui Qiu ◽  
Jun Jiang ◽  
Shun-ren Xia
Keyword(s):  
Intravascular Ultrasound ◽  
Feature Detection ◽  
Guide Wire ◽  
Disease Diagnosis ◽  
Rectangular Domain ◽  
Ultrasound Images ◽  
Snake Model ◽  
External Force Field ◽  
The Media ◽  
Learning Machine

The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.

scholarly journals Analysis of Cardiovascular Tissue Components for the Diagnosis of Coronary Vulnerable Plaque from Intravascular Ultrasound Images

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Ju Hwan Lee ◽  
Yoo Na Hwang ◽  
Ga Young Kim ◽  
Eun Seok Shin ◽  
Sung Min Kim
Keyword(s):  
Intravascular Ultrasound ◽  
Vulnerable Plaque ◽  
Tissue Characterization ◽  
Plaque Rupture ◽  
Fibrous Tissue ◽  
Elastic Membrane ◽  
Ultrasound Images ◽  
Tissue Properties ◽  
Cardiovascular Tissue ◽  
The Media

The purpose of this study was to characterize cardiovascular tissue components and analyze the different tissue properties for predicting coronary vulnerable plaque from intravascular ultrasound (IVUS) images. For this purpose, sequential IVUS image frames were obtained from human coronary arteries using 20 MHz catheters. The plaque regions between the intima and media-adventitial borders were manually segmented in all IVUS images. Tissue components of the plaque regions were classified into having fibrous tissue (FT), fibrofatty tissue (FFT), necrotic core (NC), or dense calcium (DC). The media area and lumen diameter were also estimated simultaneously. In addition, the external elastic membrane (EEM) was computed to predict the vulnerable plaque after the tissue characterization. The reliability of manual segmentation was validated in terms of inter- and intraobserver agreements. The quantitative results found that the FT and the media as well as the NC would be good indicators for predicting vulnerable plaques in IVUS images. In addition, the lumen was not suitable for early diagnosis of vulnerable plaque because of the low significance compared to the other vessel parameters. To predict vulnerable plaque rupture, future study should have additional experiments using various tissue components, such as the EEM, FT, NC, and media.

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

scholarly journals 950-93 Automatic Identification of Calcified Regions on Intravascular Ultrasound Images of Human Coronary Arteries Using Texture Analysis

1995 ◽  
Vol 25 (2) ◽  
pp. 180A
Author(s):  
Marco Masseroli ◽  
Robert M. Cothren ◽  
E. Murat Tuzcu ◽  
Dominique S. Meier ◽  
James D. Thomas ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Intravascular Ultrasound ◽  
Coronary Arteries ◽  
Automatic Identification ◽  
Ultrasound Images

Automated lumen definition from 30 MHz intravascular ultrasound images

1997 ◽  
Vol 1 (4) ◽  
pp. 363-377 ◽  
Author(s):  
Carolien J. Bouma ◽  
Wiro J. Niessen ◽  
Karel J. Zuiderveld ◽  
Elma J. Gussenhoven ◽  
Max A. Viergever
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Images

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

scholarly journals Automatic border detection of intravascular ultrasound images

1996 ◽  
Vol 27 (2) ◽  
pp. 240
Author(s):  
Stephen P. Wiet ◽  
Stuart A. Greenfield ◽  
Reena Sinha ◽  
Gorav Ailawadi ◽  
Michael J. Vonesh ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Images ◽  
Border Detection
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