Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels

09/08/2017
by   Hamid R. Fazlali, et al.
0

Coronary artery disease (CAD) is the leading causes of death around the world. One of the most common imaging methods for diagnosing this disease is X-ray angiography. Diagnosing using these images is usually challenging due to non-uniform illumination, low contrast, presence of other body tissues, presence of catheter etc. These challenges make the diagnoses task of cardiologists tougher and more prone to misdiagnosis. In this paper we propose a new automated framework for coronary arteries segmentation, catheter detection and center-line extraction in x-ray angiography images. Our proposed segmentation method is based on superpixels. In this method at first three different superpixel scales are exploited and a measure for vesselness probability of each superpixel is determined. A majority voting is used for obtaining an initial segmentation map from these three superpixel scales. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it. Also we use the image ridges for extracting the coronary arteries centerlines. We evaluated our method qualitatively and quantitatively on two different challenging datasets and compared it with one of the previous well-known coronary arteries segmentation methods. Our method could detect the catheter and reduced the false positive rate in addition to achieving better segmentation results. The evaluation results prove that our method performs better in a much shorter time.

READ FULL TEXT

page 5

page 6

page 7

page 9

page 10

page 11

page 12

page 13

research
11/13/2021

Developing a Novel Approach for Periapical Dental Radiographs Segmentation

Image processing techniques has been widely used in dental researches su...
research
07/17/2017

Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common...
research
02/28/2022

Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework

New production techniques have emerged that have made it possible to pro...
research
11/21/2016

Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization

Ridge and valley enhancing filters are widely used in applications such ...
research
02/21/2018

Liver Segmentation in Abdominal CT Images by Adaptive 3D Region Growing

Automatic liver segmentation plays an important role in computer-aided d...
research
07/14/2023

ConTrack: Contextual Transformer for Device Tracking in X-ray

Device tracking is an important prerequisite for guidance during endovas...
research
04/27/2020

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

In this paper, we compare and evaluate different testing protocols used ...

Please sign up or login with your details

Forgot password? Click here to reset