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Published on: November 1, 2018
A novel segmentation method using multiresolution analysis with 3D visualization for X-ray coronary angiogram images
S Nirmaladevi1, P Lavanya, N Kumaravel
1Department of Electronics and Communication Engineering, College of Engineering, Anna University, Chennai, India. nirmala_1969@hotmail.com
Insights
This study introduces an advanced automatic segmentation technique for coronary angiography, improving the accuracy of detecting arterial narrowing caused by atherosclerosis. Enhanced 3D visualization aids in precise diagnosis and treatment planning for cardiac diseases.
Area of Science:
- Medical Imaging
- Cardiovascular Medicine
- Image Processing
Background:
- Coronary angiography is crucial for diagnosing and treating cardiac diseases, primarily atherosclerosis.
- Atherosclerosis narrows coronary arteries, reducing blood supply to the heart muscle.
- Accurate lumen narrowing determination relies heavily on image segmentation quality.
Purpose of the Study:
- To develop an automatic and accurate image segmentation technique for coronary angiograms.
- To incorporate 3D visualization for clearer assessment of lesion shape and severity.
- To enhance the diagnostic precision of coronary artery disease (CAD).
Main Methods:
- Proposed a novel multithresholding approach for image segmentation.
- Integrated entropy measure and multiresolution analysis for automated segmentation.
- Conducted segmentation performance analysis comparing various methods.
Main Results:
- The proposed multithresholding technique demonstrated superior accuracy in segmenting coronary angiographic images.
- Achieved better identification of arterial blocks compared to traditional methods.
- 3D visualization provided enhanced insights into lesion characteristics.
Conclusions:
- The developed automatic segmentation technique offers improved accuracy for coronary artery disease diagnosis.
- Multithresholding with entropy and multiresolution analysis effectively overcomes limitations of other methods.
- This approach facilitates more precise treatment planning through enhanced 3D visualization.
Abstract:
Coronary angiography is a widely used tool in the diagnosis and treatment of cardiac diseases. The main cause of coronary artery disease is atherosclerosis, which leads to the narrowing of artery lumen, resulting in decreased blood supply to heart muscles. Determination of narrowing of the lumens mainly depends upon the quality of the segmented image; with improved segmentation technique there is better accuracy in identification of blocks. The main purpose of the paper is to develop an automatic, accurate segmentation technique with 3D visualization for the segmented images. 3D visualization provides clearer information regarding the shape and severity of the lesion. The thresholding technique is one of the oldest and simplest techniques used for segmentation. This paper proposes a multithresholding approach using the entropy measure and multiresolution analysis to ensure automatic and accurate segmentation by overcoming some of the problems encountered in other techniques. Also, segmentation performance analysis was conducted for various segmentation methods. This method is tested with different real coronary angiographic images and was found to perform better than the other techniques.
