ArticleJournal of medical systems2020
Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 52 citations in OpenAlex.
- Guided sparse decomposition with anisotropic fusion for medical image enhancement.Quantitative imaging in medicine and surgery · 2025Article
- A new hybrid image denoising algorithm using adaptive and modified decision-based filters for enhanced image quality.Scientific reports · 2025Article
- Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model.IEEE access : practical innovations, open solutions · 2025Article
- Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.BMC medical imaging · 2024Article
- Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm.Yonsei medical journal · 2024Article
- An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal.Sensors (Basel, Switzerland) · 2023Article
- Evaluation of histogram equalization and contrast limited adaptive histogram equalization effect on image quality and fractal dimensions of digital periapical radiographs.Oral radiology · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.