Stereoscopic augmented reality for single camera endoscopy: a virtual study

Wang, Yen Yu; Kumar, Atul; Liu, Kai Che; Huang, Shih Wei; Huang, Chia‐Wei; Su, Wei; Hsiao, Fu‐Li; Lie, Wen Nung

doi:10.1080/21681163.2016.1197798

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The 2D conventional endoscopy does not provide any depth information of the surgical scene. Therefore, Augmented Reality (AR) techniques have been employed to address these limitations [649]. Moreover, CT scans can be used to non-invasively reconstruct the corresponding virtual colonoscopy, such as in the case of polyp detection [593].…”

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confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

102

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce 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. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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confidence: 99%