Supervised Segmentation of Retinal Vessel Structures Using ANN

• URL: http://arxiv.org/abs/2001.05549v1
• Date: Wed, 15 Jan 2020 20:48:03 GMT
• Title: Supervised Segmentation of Retinal Vessel Structures Using ANN
• Authors: Esra Kaya, \.Ismail Sar{\i}ta\c{s}, Ilker Ali Ozkan
• Abstract summary: The study was performed using 20 images in the DRIVE data set which is one of the most common retina data sets known. The average segmentation accuracy for 20 images was found as 0.9492.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, a supervised retina blood vessel segmentation process was performed on the green channel of the RGB image using artificial neural network (ANN). The green channel is preferred because the retinal vessel structures can be distinguished most clearly from the green channel of the RGB image. The study was performed using 20 images in the DRIVE data set which is one of the most common retina data sets known. The images went through some preprocessing stages like contrastlimited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering to obtain a good segmentation. Retinal vessel structures were highlighted with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. Then, the network was trained by the binary version of the images specified as training images in the dataset and the targets are the images segmented manually by a specialist. The average segmentation accuracy for 20 images was found as 0.9492.

Related papers

• NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful [15.10324445908774]
  In this paper, we analyze a popular NCT-CRC-HE-100K colorectal cancer dataset used in numerous prior works. We show that both this dataset and the obtained results may be affected by data-specific biases. We show that even the simplest model using only 3 features per image can demonstrate over 50% accuracy on this 9-class dataset.
  arXiv  Detail & Related papers  (2024-09-17T20:36:03Z)
• Image Denoising Using Green Channel Prior [3.8541941705185114]
  Green channel prior-based image denoising (GCP-ID) method integrates GCP into classic patch-based denoising framework. To enhance the adaptivity of GCP-ID to various image contents, we cast the noise estimation problem into a classification task and train an effective estimator based on convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2024-08-12T05:07:12Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Iris super-resolution using CNNs: is photo-realism important to iris recognition? [67.42500312968455]
  Single image super-resolution techniques are emerging, especially with the use of convolutional neural networks (CNNs) In this work, the authors explore single image super-resolution using CNNs for iris recognition. They validate their approach on a database of 1.872 near infrared iris images and on a mobile phone image database.
  arXiv  Detail & Related papers  (2022-10-24T11:19:18Z)
• A Trio-Method for Retinal Vessel Segmentation using Image Processing [0.0]
  This paper primarily focuses on the segmentation of retinal vessels using a triple preprocessing approach. Two proposed U-Net architectures were compared in terms of all the standard performance metrics. This real-time deployment can help in the efficient pre-processing of images with better segmentation and detection.
  arXiv  Detail & Related papers  (2022-09-19T22:07:34Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Universal Deep Network for Steganalysis of Color Image based on Channel Representation [41.86330101334733]
  We design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2021-11-24T02:22:13Z)
• Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation [0.9322743017642274]
  The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be used to transform endoscopic images of phantoms into images which more closely resemble the intra-operative appearance of the same surgical target structure. We show that a task defined on sparse landmark labels improves consistency of synthesis by the generator network in both domains.
  arXiv  Detail & Related papers  (2021-07-14T19:09:50Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• Learning to Structure an Image with Few Colors [59.34619548026885]
  We propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. With only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime.
  arXiv  Detail & Related papers  (2020-03-17T17:56:15Z)
• Supervised and Unsupervised Learning of Parameterized Color Enhancement [112.88623543850224]
  We tackle the problem of color enhancement as an image translation task using both supervised and unsupervised learning. We achieve state-of-the-art results compared to both supervised (paired data) and unsupervised (unpaired data) image enhancement methods on the MIT-Adobe FiveK benchmark. We show the generalization capability of our method, by applying it on photos from the early 20th century and to dark video frames.
  arXiv  Detail & Related papers  (2019-12-30T13:57:06Z)

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.