Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization

• URL: http://arxiv.org/abs/2411.01373v1
• Date: Sat, 02 Nov 2024 22:20:56 GMT
• Title: Medical X-Ray Image Enhancement Using Global Contrast-Limited Adaptive Histogram Equalization
• Authors: Sohrab Namazi Nia, Frank Y. Shih,
• Abstract summary: G-CLAHE (Global-Contrast Limited Adaptive Histogram Equalization) perfectly suits medical imaging with a focus on X-rays. This method adapts from Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to take both advantages and avoid weakness.
• Score: 0.7596606040729642
• License:
• Abstract: In medical imaging, accurate diagnosis heavily relies on effective image enhancement techniques, particularly for X-ray images. Existing methods often suffer from various challenges such as sacrificing global image characteristics over local image characteristics or vice versa. In this paper, we present a novel approach, called G-CLAHE (Global-Contrast Limited Adaptive Histogram Equalization), which perfectly suits medical imaging with a focus on X-rays. This method adapts from Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to take both advantages and avoid weakness to preserve local and global characteristics. Experimental results show that it can significantly improve current state-of-the-art algorithms to effectively address their limitations and enhance the contrast and quality of X-ray images for diagnostic accuracy.

Related papers

• Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping [0.282736966249181]
  inconsistency in the initial presentation of X-ray images is a common complaint by radiologists. Existing deep-learning-based end-to-end solutions can automatically correct images with promising performances. A novel interpretable mapping method by deep learning is proposed, which automatically enhances the image brightness and contrast globally and locally.
  arXiv  Detail & Related papers  (2025-01-21T16:04:53Z)
• Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis [2.15242029196761]
  Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical. We propose a novel multi-feature fusion network using parallel attention blocks to fuse the original CXR images and local-phase feature-enhanced CXR images at multi-scales.
  arXiv  Detail & Related papers  (2023-04-25T16:56:12Z)
• Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors [49.005337470305584]
  During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. The variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. We propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases.
  arXiv  Detail & Related papers  (2022-08-22T13:57:04Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Data-Consistent Local Superresolution for Medical Imaging [5.025654873456756]
  After a reconstruction of a full tomographic image, the clinician may believe that some critical parts of the image are not clear enough, and may wish to see clearer these regions-of-interest. A naive approach (which is highly not recommended) would be performing the global reconstruction of a higher resolution image. We propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest.
  arXiv  Detail & Related papers  (2022-02-22T13:18:38Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• Multi-label Thoracic Disease Image Classification with Cross-Attention Networks [65.37531731899837]
  We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images. We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
  arXiv  Detail & Related papers  (2020-07-21T14:37:00Z)
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis [102.40869566439514]
  We seek to exploit rich labeled data from relevant domains to help the learning in the target task via Unsupervised Domain Adaptation (UDA) Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
  arXiv  Detail & Related papers  (2020-07-05T11:49:17Z)

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.