Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

From LRDE

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. Yetmost 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.

Documents

Bibtex (lrde.bib)

@Article{	  shi.21.itip,
  author	= {Tianyi Shi and Nicolas Boutry and Yongchao Xu and Thierry
		  G\'eraud},
  title		= {Local Intensity Order Transformation for Robust
		  Curvilinear Object Segmentation},
  journal	= {IEEE Transactions on Image Processing},
  year		= {2022},
  volume	= {31},
  pages		= {2557--2569},
  month		= mar,
  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
		  \url{https://github.com/TY-Shi/LIOT}.},
  url		= {10.1109/TIP.2022.3155954}
}