scholarly journals Automatic Identification of the Lumen Border in Intravascular Ultrasound Images

2012 ◽  
Vol 19B (3) ◽  
pp. 201-208
Author(s):  
Jun-Oh Park ◽  
Byoung-Chul Ko ◽  
Hee-Jun Park ◽  
Jae-Yeal Nam
Keyword(s):  
Intravascular Ultrasound ◽  
Automatic Identification ◽  
Ultrasound Images

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

Automatic identification of vulnerable plaques based on intravascular ultrasound images

2011 ◽  
Vol 19 (10) ◽  
pp. 2507-2519 ◽  
Author(s):  
张麒 ZHANG Qi ◽  
汪源源 WANG Yuan-yuan ◽  
马剑英 MA Jian-ying ◽  
钱菊英 QIAN Ju-ying ◽  
施俊 SHI Jun ◽  
...  
Keyword(s):  
Intravascular Ultrasound ◽  
Automatic Identification ◽  
Ultrasound Images ◽  
Vulnerable Plaques

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%.

Improvement of the classification quality in detection of Hashimoto’s disease with a combined classifier approach

2017 ◽  
Vol 231 (8) ◽  
pp. 774-782 ◽  
Author(s):  
Zbigniew Omiotek
Keyword(s):  
Computer System ◽  
Majority Vote ◽  
Classification Error ◽  
Automatic Identification ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Linear Discriminant ◽  
Nearest Neighbours ◽  
Combined Classifier ◽  
Classification Quality

The purpose of the study was to construct an efficient classifier that, along with a given reduced set of discriminant features, could be used as a part of the computer system in automatic identification and classification of ultrasound images of the thyroid gland, which is aimed to detect cases affected by Hashimoto’s thyroiditis. A total of 10 supervised learning techniques and a majority vote for the combined classifier were used. Two models were proposed as a result of the classifier’s construction. The first one is based on the K-nearest neighbours method (for K = 7). It uses three discriminant features and affords sensitivity equal to 88.1%, specificity of 66.7% and classification error at a level of 21.8%. The second model is a combined classifier, which was constructed using three-component classifiers. They are based on the K-nearest neighbours method (for K = 7), linear discriminant analysis and a boosting algorithm. The combined classifier is based on 48 discriminant features. It allows to achieve the classification sensitivity equal to 88.1%, specificity of 69.4% and classification error at a level of 20.5%. The combined classifier allows to improve the classification quality compared to the single model. The models, built as a part of the automatic computer system, may support the physician, especially in first-contact hospitals, in diagnosis of cases that are difficult to recognise based on ultrasound images. The high sensitivity of constructed classification models indicates high detection accuracy of the sick cases, and this is beneficial to the patients from a medical point of view.

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

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 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

Intravascular Ultrasound: Potential Tool to Assess Coronary Anastomosis Quality

2003 ◽  
Vol 11 (2) ◽  
pp. 143-146
Author(s):  
Piergiorgio Tozzi ◽  
Antonio F Corno ◽  
Ludwig K von Segesser
Keyword(s):  
Coronary Artery ◽  
Intravascular Ultrasound ◽  
Left Internal Mammary Artery ◽  
Artery Bypass ◽  
Ultrasound Images ◽  
Cross Sectional ◽  
Flow Measurements ◽  
Off Pump ◽  
The Mean ◽  
Macroscopic Examination

Coronary angiography and Doppler flow measurements are most commonly used to assess the patency of anastomoses in the operating theater. Intravascular ultrasound might be another means of monitoring the surgical procedure during coronary artery bypass. Five sheep underwent off-pump bypass of the left anterior descending coronary artery using the left internal mammary artery. The running suture was evaluated by intraoperative fluoroscopy and a coronary intravascular ultrasound probe inserted into the target artery proximal to the anastomosis. Macroscopic examination of the anastomosis was performed to validate the angiographic and intravascular ultrasound images. The diameter, cross-sectional area, and compliance of each anastomosis were calculated in systole and diastole. All anastomoses were patent without signs of stenosis. In one case, intravascular ultrasound showed an intimal flap, which was confirmed by macroscopic examination. The mean major anastomotic diameter was 4.5 ± 0.5 mm on angiography and 4.0 ± 0.5 mm on intravascular ultrasound. From the ultrasound data, the mean cross-sectional anastomotic area was calculated as 6.21 ± 0.1 mm2 in systole and 5.49 ± 0.1 mm2 in diastole, and these data were used to calculate the cross-sectional anastomosis compliance. Coronary intravascular ultrasound can visualize intima-to-intima apposition and provide reliable calculations of anastomosis compliance.

An Automated Robust Segmentation Method for Intravascular Ultrasound Images

2014 ◽  
pp. 407-426
Author(s):  
Prakash Manandhar ◽  
Chi Hau Chen ◽  
Ahmet Umit Coskun ◽  
Uvais A. Qidwai
Keyword(s):  
Intravascular Ultrasound ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Robust Segmentation
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