19 research outputs found
Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities
Intensity inhomogeneities in images constitute a considerable challenge in
image segmentation. In this paper we propose a novel biconvex variational model
to tackle this task. We combine a total variation approach for multi class
segmentation with a multiplicative model to handle the inhomogeneities. Our
method assumes that the image intensity is the product of a smoothly varying
part and a component which resembles important image structures such as edges.
Therefore, we penalize in addition to the total variation of the label
assignment matrix a quadratic difference term to cope with the smoothly varying
factor. A critical point of our biconvex functional is computed by a modified
proximal alternating linearized minimization method (PALM). We show that the
assumptions for the convergence of the algorithm are fulfilled by our model.
Various numerical examples demonstrate the very good performance of our method.
Particular attention is paid to the segmentation of 3D FIB tomographical images
which was indeed the motivation of our work
Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
This paper studies a diffusion-based framework to address the low-light image
enhancement problem. To harness the capabilities of diffusion models, we delve
into this intricate process and advocate for the regularization of its inherent
ODE-trajectory. To be specific, inspired by the recent research that low
curvature ODE-trajectory results in a stable and effective diffusion process,
we formulate a curvature regularization term anchored in the intrinsic
non-local structures of image data, i.e., global structure-aware
regularization, which gradually facilitates the preservation of complicated
details and the augmentation of contrast during the diffusion process. This
incorporation mitigates the adverse effects of noise and artifacts resulting
from the diffusion process, leading to a more precise and flexible enhancement.
To additionally promote learning in challenging regions, we introduce an
uncertainty-guided regularization technique, which wisely relaxes constraints
on the most extreme regions of the image. Experimental evaluations reveal that
the proposed diffusion-based framework, complemented by rank-informed
regularization, attains distinguished performance in low-light enhancement. The
outcomes indicate substantial advancements in image quality, noise suppression,
and contrast amplification in comparison with state-of-the-art methods. We
believe this innovative approach will stimulate further exploration and
advancement in low-light image processing, with potential implications for
other applications of diffusion models. The code is publicly available at
https://github.com/jinnh/GSAD.Comment: Accepted to NeurIPS 202
Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation
Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal
blood vessel detection, vascular network topology estimation, and arteries / veins classi cation
are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide
spectrum of diseases.
Methods: We propose a new framework for precisely segmenting retinal vasculatures,
constructing retinal vascular network topology, and separating the arteries and veins. A
non-local total variation inspired Retinex model is employed to remove the image intensity
inhomogeneities and relatively poor contrast. For better generalizability and segmentation
performance, a superpixel based line operator is proposed as to distinguish between lines and
the edges, thus allowing more tolerance in the position of the respective contours. The concept
of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel
network into arteries and veins.
Results: The proposed segmentation method yields competitive results on three pub-
lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com-
pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964,
respectively. The topology estimation approach has been applied to ve public databases
1
(DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830,
0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation
based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and
VICAVR) are 0.90.9, 0.910, and 0.907, respectively.
Conclusions: The experimental results show that the proposed framework has e ectively
addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon-
struction. The vascular topology information signi cantly improves the accuracy on arteries
/ veins classi cation
Saliency Driven Vasculature Segmentation with Infinite Perimeter Active Contour Model
Automated detection of retinal blood vessels plays an important role in advancing the understanding of the mechanism, diagnosis and treatment of cardiovascular disease and many systemic diseases, such as diabetic retinopathy and age-related macular degeneration. Here, we propose a new framework for precisely segmenting retinal vasculatures. The proposed framework consists of three steps. A non-local total variation model is adapted to the Retinex theory, which aims to address challenges presented by intensity inhomogeneities, and the relatively low contrast of thin vessels compared to the background. The image is then divided into superpixels, and a compactness-based saliency detection method is proposed to locate the object of interest. For better general segmentation performance, we then make use of a new infinite active contour model to segment the vessels in each superpixel. The proposed framework has wide applications, and the results show that our model outperforms its competitors