Pixel Relationships-based Regularizer for Retinal Vessel Image
Segmentation
- URL: http://arxiv.org/abs/2212.13731v1
- Date: Wed, 28 Dec 2022 07:35:20 GMT
- Title: Pixel Relationships-based Regularizer for Retinal Vessel Image
Segmentation
- Authors: Lukman Hakim, Takio Kurita
- Abstract summary: This study presents regularizers to give the pixel neighbor relationship information to the learning process.
Experiments show that our scheme successfully captures pixel neighbor relations and improves the performance of the convolutional neural network.
- Score: 4.3251090426112695
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The task of image segmentation is to classify each pixel in the image based
on the appropriate label. Various deep learning approaches have been proposed
for image segmentation that offers high accuracy and deep architecture.
However, the deep learning technique uses a pixel-wise loss function for the
training process. Using pixel-wise loss neglected the pixel neighbor
relationships in the network learning process. The neighboring relationship of
the pixels is essential information in the image. Utilizing neighboring pixel
information provides an advantage over using only pixel-to-pixel information.
This study presents regularizers to give the pixel neighbor relationship
information to the learning process. The regularizers are constructed by the
graph theory approach and topology approach: By graph theory approach, graph
Laplacian is used to utilize the smoothness of segmented images based on output
images and ground-truth images. By topology approach, Euler characteristic is
used to identify and minimize the number of isolated objects on segmented
images. Experiments show that our scheme successfully captures pixel neighbor
relations and improves the performance of the convolutional neural network
better than the baseline without a regularization term.
Related papers
- Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation [78.54656076915565]
Topological correctness plays a critical role in many image segmentation tasks.
Most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy.
We propose a novel, graph-based framework for topologically accurate image segmentation.
arXiv Detail & Related papers (2024-11-05T16:20:14Z) - Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization [4.8454936010479335]
We propose a Multi-view Pixel-wise Contrastive algorithm (MPC) for image forgery localization.
Specifically, we first pre-train the backbone network with the supervised contrastive loss.
Then the localization head is fine-tuned using the cross-entropy loss, resulting in a better pixel localizer.
arXiv Detail & Related papers (2024-06-19T13:51:52Z) - Patch-wise Graph Contrastive Learning for Image Translation [69.85040887753729]
We exploit the graph neural network to capture the topology-aware features.
We construct the graph based on the patch-wise similarity from a pretrained encoder.
In order to capture the hierarchical semantic structure, we propose the graph pooling.
arXiv Detail & Related papers (2023-12-13T15:45:19Z) - Pixel-Inconsistency Modeling for Image Manipulation Localization [59.968362815126326]
Digital image forensics plays a crucial role in image authentication and manipulation localization.
This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts.
Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
arXiv Detail & Related papers (2023-09-30T02:54:51Z) - Single-Image Super-Resolution Reconstruction based on the Differences of
Neighboring Pixels [3.257500143434429]
The deep learning technique was used to increase the performance of single image super-resolution (SISR)
In this paper, we propose the differences of neighboring pixels to regularize the CNN by constructing a graph from the estimated image and the ground-truth image.
The proposed method outperforms the state-of-the-art methods in terms of quantitative and qualitative evaluation of the benchmark datasets.
arXiv Detail & Related papers (2022-12-28T07:30:07Z) - From Explanations to Segmentation: Using Explainable AI for Image
Segmentation [1.8581514902689347]
We build upon the advances of the Explainable AI (XAI) community and extract a pixel-wise binary segmentation.
We show that we achieve similar results compared to an established U-Net segmentation architecture.
The proposed method can be trained in a weakly supervised fashion, as the training samples must be only labeled on image-level.
arXiv Detail & Related papers (2022-02-01T10:26:10Z) - Maximize the Exploration of Congeneric Semantics for Weakly Supervised
Semantic Segmentation [27.155133686127474]
We construct a graph neural network (P-GNN) based on the self-detected patches from different images that contain the same class labels.
We conduct experiments on the popular PASCAL VOC 2012 benchmarks, and our model yields state-of-the-art performance.
arXiv Detail & Related papers (2021-10-08T08:59:16Z) - AINet: Association Implantation for Superpixel Segmentation [82.21559299694555]
We propose a novel textbfAssociation textbfImplantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids.
Our method could not only achieve state-of-the-art performance but maintain satisfactory inference efficiency.
arXiv Detail & Related papers (2021-01-26T10:40:13Z) - Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation.
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths.
In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
arXiv Detail & Related papers (2020-12-09T12:40:13Z) - Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning [86.45526827323954]
Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training.
We propose an iterative algorithm to learn such pairwise relations.
We show that the proposed algorithm performs favorably against the state-of-the-art methods.
arXiv Detail & Related papers (2020-02-19T10:32:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.