Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

Abstract: Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmentation for road condition evaluation and maintenance. Currently, deep learning-based methods have achieved impressive performance on these tasks. Yet, most of them mainly focus on finding powerful deep architectures but ignore capturing the inherent curvilinear structure feature (e.g., the curvilinear structure is darker than the context) for a more robust representation. In consequence, the performance usually drops a lot on cross-datasets, which poses great challenges in practice. In this paper, we aim to improve the generalizability by introducing a novel local intensity order transformation (LIOT). Specifically, we transfer a gray-scale image into a contrast-invariant four-channel image based on the intensity order between each pixel and its nearby pixels along with the four (horizontal and vertical) directions. This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes. Cross-dataset evaluation on three retinal blood vessel segmentation datasets demonstrates that LIOT improves the generalizability of some state-of-the-art methods. Additionally, the cross-dataset evaluation between retinal blood vessel segmentation and pavement crack segmentation shows that LIOT is able to preserve the inherent characteristic of curvilinear structure with large appearance gaps. An implementation of the proposed method is available at https://github.com/TY-Shi/LIOT.

• The paper introduces LIOT, an image transformation that converts gray-scale images into a four-channel representation using local intensity comparisons.
• It enhances robustness and generalizability in segmenting curvilinear structures across diverse datasets, improving metrics like sensitivity, accuracy, and F1-score.
• The study demonstrates that leveraging inherent intensity patterns preserves structural integrity, suggesting promising applications in real-world segmentation tasks.

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

The segmentation of curvilinear objects, such as blood vessels in retinal images or cracks in pavement, poses significant challenges due to their thin and tortuous shapes, inadequate contrast with the background, and variable image appearances. This paper introduces a novel image transformation method, the Local Intensity Order Transformation (LIOT), designed to enhance the robustness and generalizability of state-of-the-art curvilinear structure segmentation methods.

Methodology

LIOT is a transformation that converts a gray-scale image into a four-channel representation based on the intensity order between each pixel and its neighboring pixels along four directions: horizontal and vertical. This representation is invariant to contrast changes, which is advantageous for capturing the inherent characteristics of curvilinear structures, often distinguished by being darker than their surrounding context.

The key steps of the LIOT are:

1. Transform the input image into a four-channel image using local intensity comparisons along specified directions.
2. Utilize this transformed representation as input to a convolutional neural network (CNN) optimized for curvilinear structure segmentation.

Experimental Results

The efficacy of LIOT was evaluated through cross-dataset experiments on three retinal blood vessel segmentation datasets (DRIVE, STARE, CHASEDB1) and the CrackTree pavement crack dataset.

• Cross-Retinal Dataset Evaluation: LIOT demonstrated enhanced generalization capabilities, outperforming baseline deep learning models on cross-dataset tests regarding sensitivity, accuracy, and F1-score. Notably, LIOT improved the connectivity of segmented curvilinear structures, indicating better structural integrity preservation.
• Large Domain Gap Evaluation: Testing LIOT between datasets with significant appearance differences, such as CrackTree and retinal images, further validated its robustness. LIOT managed to maintain considerable performance even when applied to domains not present in the training data, exhibiting resilience against domain shifts that typically degrade performance in conventional deep learning approaches.

Implications and Future Directions

The introduction of LIOT addresses a persistent challenge in curvilinear object segmentation: the fidelity of model performance across datasets with varying contrast and appearance characteristics. This paper proposes a straightforward yet effective image transformation that can be coupled with existing deep learning architectures, thereby improving their applicability in real-world scenarios where cross-dataset variability is evident.

Theoretical implications include reevaluating the emphasis placed on architecture complexity relative to input representation in model generalization and robustness. LIOT showcases that leveraging inherent data characteristics through informed pre-processing can substantially augment model performance.

Moving forward, exploring LIOT's applicability in other domains where capturing intrinsic data characteristics under varying conditions is critical could be worthwhile. Additionally, future research could focus on extending LIOT to handle additional challenges posed by three-dimensional curvilinear structures or incorporating it within adversarial training frameworks to further bolster model robustness.