Local Intensity Order Transformation for Robust Curvilinear Object Segmentation.

Area of Science:

• Computer Vision
• Image Processing
• Machine Learning

Background:

• Segmentation of curvilinear structures is crucial for applications like retinal blood vessel analysis and pavement defect detection.
• Current deep learning methods often lack robustness across different datasets due to insufficient focus on inherent structural features.

Purpose of the Study:

• To enhance the generalizability of deep learning models for curvilinear structure segmentation.
• To introduce a novel representation that captures inherent structural characteristics and is robust to contrast variations.

Main Methods:

• A novel Local Intensity Order Transformation (LIOT) is proposed.
• LIOT transforms grayscale images into a contrast-invariant four-channel representation based on local pixel intensity ordering in four directions.
• This method preserves the inherent features of curvilinear structures.

Main Results:

• LIOT demonstrated improved cross-dataset generalizability for retinal blood vessel segmentation.
• Evaluations showed LIOT effectively preserves curvilinear structure characteristics across different applications, including pavement crack segmentation.
• The proposed method enhances the robustness of state-of-the-art segmentation techniques.

Conclusions:

• Local Intensity Order Transformation (LIOT) offers a robust approach to segmenting curvilinear structures.
• The method significantly improves model generalizability, addressing challenges posed by cross-dataset variations.
• LIOT shows promise for real-world applications requiring reliable segmentation of linear features.