Vessel Segmentation Method in Coronography Images

Area of Science:

• Cardiology
• Medical Imaging
• Image Processing

Background:

• Coronary angiography is crucial for diagnosing cardiovascular diseases.
• Precise segmentation of coronary arteries is challenging due to image noise, contrast variations, and anatomical overlap.

Purpose of the Study:

• To develop an innovative algorithm for generating accurate artery masks from coronary angiography images.
• To enhance the precision of coronary vessel definition for improved diagnostic capabilities.

Main Methods:

• The proposed algorithm utilizes advanced image processing techniques.
• Key methods include edge detection, morphological transformations, and adaptive filtering for efficient arterial structure isolation.

Main Results:

• The algorithm demonstrated superior accuracy and resilience in segmenting coronary arteries compared to conventional methods.
• Evaluation on angiographic datasets confirmed the effectiveness of the proposed approach.

Conclusions:

• The developed methodology shows significant potential for supporting automated diagnostic tools in cardiology.
• This technique can enhance clinical decision-making processes for cardiovascular diseases.