Journal ArticleDOI
Local gray level S-curve transformation A generalized contrast enhancement technique for medical images
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TLDR
The local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues, and can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.About:
This article is published in Computers in Biology and Medicine.The article was published on 2017-04-01. It has received 64 citations till now. The article focuses on the topics: Image gradient & Grayscale.read more
Citations
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Journal ArticleDOI
MedGA: A novel evolutionary method for image enhancement in medical imaging systems
Leonardo Rundo,Leonardo Rundo,Andrea Tangherloni,Marco S. Nobile,Carmelo Militello,Daniela Besozzi,Giancarlo Mauri,Paolo Cazzaniga +7 more
TL;DR: This work introduces MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions.
Journal ArticleDOI
A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization
TL;DR: The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity.
Journal ArticleDOI
Hospital evaluation mechanism based on mobile health for IoT system in social networks.
TL;DR: A model of the hospital confidence evaluation index is established by combining national evaluation and a third-party evaluation and is applied to a social network, and the balanced distribution of the medical staff is realized.
Journal ArticleDOI
A novel framework for MR image segmentation and quantification by using MedGA.
Leonardo Rundo,Andrea Tangherloni,Andrea Tangherloni,Andrea Tangherloni,Paolo Cazzaniga,Marco S. Nobile,Giorgio Ivan Russo,Maria Carla Gilardi,Salvatore Vitabile,Giancarlo Mauri,Daniela Besozzi,Carmelo Militello +11 more
TL;DR: A novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation is presented, which is here applied to different clinical scenarios involving bimodal MR image analysis: uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and brain metastatic cancer segmentsation in neuro-radiosurgery therapy.
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A new approach for medical image enhancement based on luminance-level modulation and gradient modulation
TL;DR: Experimental results on CT images, X-ray images and MRI images from medical image datasets and quantitative analyses by structural similarity index measurement, average gradient, relative enhancement in contrast (REC) and information entropy demonstrate that the results of the proposed LM&GM method are competitive and overwhelm those of the existing methods.
References
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Journal ArticleDOI
FSIM: A Feature Similarity Index for Image Quality Assessment
TL;DR: A novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features.
Journal ArticleDOI
Contrast enhancement using brightness preserving bi-histogram equalization
TL;DR: It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
The Mammographic Image Analysis Society digital mammogram database
John Suckling,J. Parker,S. Astley,I. Hutt,C. Boggis,Ian W. Ricketts,E. Stamatakis,N. Cerneaz,SL Kok,P. Taylor,D. Betal,J. Savage +11 more
Journal ArticleDOI
Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation
TL;DR: Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.
Journal ArticleDOI
Medical Image Fusion: A survey of the state of the art
TL;DR: In this article, a review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion, concluding that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
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