Medical images edge detection based on mathematical morphology

Area of Science:

• Medical Imaging Analysis
• Image Processing
• Computational Anatomy

Background:

• Edge detection is crucial for medical image analysis, including organ recognition, segmentation, and 3D reconstruction.
• Traditional methods like gradient-based and template-based algorithms struggle with noise in medical images.
• Salt-and-pepper noise significantly degrades the accuracy of edge detection in CT scans.

Purpose of the Study:

• To propose a novel mathematical morphological edge detection algorithm.
• To address the limitations of conventional methods in handling noisy medical images.
• To enhance the accuracy of edge detection specifically for lungs CT images.

Main Methods:

• Introduction to basic mathematical morphology theory and operations.
• Development of a new mathematical morphological edge detection algorithm.
• Application and testing on lungs CT images with salt-and-pepper noise.

Main Results:

• The proposed algorithm demonstrates superior performance in denoising medical images.
• It achieves more efficient and accurate edge detection compared to existing methods.
• Experimental results validate its effectiveness against template-based and general morphological algorithms.

Conclusions:

• The novel mathematical morphological algorithm is highly effective for edge detection in noisy medical images.
• It offers significant improvements for lungs CT image analysis.
• The method provides a robust solution for pre-processing in medical image segmentation and 3D reconstruction.