Medical Image Inpainting with RBF Interpolation Technique

Alsalamah, Mashail; Amin, Saad

doi:10.14569/ijacsa.2016.070814

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…The completion of such missing or distorted regions is important to enhance post-processing tasks such as segmentation or classification. Traditional approaches for medical image inpainting focus on interpolation, non-local means, diffusion techniques, and texture synthesis [19][20][21][22][23]. However, the conventional methods are confined to a single image and they do not learn from images with similar features.…”

Section: Traditional Approachmentioning

confidence: 99%

Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

et al. 2021

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Distorted medical images can significantly hamper medical diagnosis, notably in the analysis of Computer Tomography (CT) images and organ segmentation specifics. Therefore, improving diagnostic imagery accuracy and reconstructing damaged portions are important for medical diagnosis. Recently, these issues have been studied extensively in the field of medical image inpainting. Inpainting techniques are emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants, foreign objects or specular reflections during the image captures. The completion of such missing or distorted regions is important for the enhancement of post-processing tasks such as segmentation or classification. In this paper, a novel framework for medical image inpainting is presented by using a multi-task learning model for CT images targeting the learning of the shape and structure of the organs of interest. This novelty has been accomplished through simultaneous training for the prediction of edges and organ boundaries with the image inpainting, while state-of-the-art methods still focus only on the inpainting area without considering the global structure of the target organ. Therefore, our model reproduces medical images with sharp contours and exact organ locations. Consequently, our technique generates more realistic and believable images compared to other approaches. Additionally, in quantitative evaluation, the proposed method achieved the best results in the literature so far, which include a PSNR value of 43.44 dB and SSIM of 0.9818 for the square-shaped regions; a PSNR value of 38.06 dB and SSIM of 0.9746 for the arbitrary-shaped regions. The proposed model generates the sharp and clear images for inpainting by learning the detailed structure of organs. Our method was able to show how promising the method is when applying it in medical image analysis, where the completion of missing or distorted regions is still a challenging task.

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Section: Traditional Approachmentioning

confidence: 99%

Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

et al. 2021

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“…Proposed Improvement RBF method has been used successfully in previous studies for scattered data interpolation [6], [16], removing an object from image [18] and image inpainting applications [18], [19]. www.ijacsa.thesai.org Carr et al published their study about RBF interpolation for cranioplasty applications.…”

Section: A Rbf Based Interpolationmentioning

confidence: 99%

Improvement of Radial basis Function Interpolation Performance on Cranial Implant Design

Atasoy¹,

Şen²,

Nar³

et al. 2017

ijacsa

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Abstract-Cranioplasty is a neurosurgical operation for repairing cranial defects that have occurred in a previous operation or trauma. Various methods have been presented for cranioplasty from past to present. In computer-aided design based methods, quality of an implant depends on operator's talent. In mathematical model based methods, such as curvefitting and various interpolations, healthy parts of a skull are used to generate implant model. Researchers have studied to improve performance of mathematical models which are independent from operators' talent. In this study, improvement of radial basis function (RBF) interpolation performance using symmetrical data is presented. Since we focused on the improvement of RBF interpolation performance on cranial implant design, results were compared with previous studies involving the same technique. In comparison with previously presented results, difference between the computed implant model and the original skull was reduced from 7 mm to 2 mm using newly proposed approach.

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“…The ultimate objective of image inpainting is to generate realistic and coherent replacements for missing or damaged regions by leveraging the surrounding information. Various traditional approaches such as interpolation [ 1 , 2 ], patch-based methods [ 3 , 4 , 5 , 6 , 7 ], and diffusion-based methods [ 8 , 9 , 10 ] have been used. However, recent advances in deep learning algorithms, together with the availability of large-scale datasets, have opened the way for deep-learning-based approaches [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] that produce high-quality inpainted results.…”

Section: Introductionmentioning

confidence: 99%

GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal

Mahmoud

Kang

2023

Sensors

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Deep-learning-based image inpainting methods have made remarkable advancements, particularly in object removal tasks. The removal of face masks has gained significant attention, especially in the wake of the COVID-19 pandemic, and while numerous methods have successfully addressed the removal of small objects, removing large and complex masks from faces remains demanding. This paper presents a novel two-stage network for unmasking faces considering the intricate facial features typically concealed by masks, such as noses, mouths, and chins. Additionally, the scarcity of paired datasets comprising masked and unmasked face images poses an additional challenge. In the first stage of our proposed model, we employ an autoencoder-based network for binary segmentation of the face mask. Subsequently, in the second stage, we introduce a generative adversarial network (GAN)-based network enhanced with attention and Masked–Unmasked Region Fusion (MURF) mechanisms to focus on the masked region. Our network generates realistic and accurate unmasked faces that resemble the original faces. We train our model on paired unmasked and masked face images sourced from CelebA, a large public dataset, and evaluate its performance on multi-scale masked faces. The experimental results illustrate that the proposed method surpasses the current state-of-the-art techniques in both qualitative and quantitative metrics. It achieves a Peak Signal-to-Noise Ratio (PSNR) improvement of 4.18 dB over the second-best method, with the PSNR reaching 30.96. Additionally, it exhibits a 1% increase in the Structural Similarity Index Measure (SSIM), achieving a value of 0.95.

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Medical Image Inpainting with RBF Interpolation Technique

Cited by 14 publications

References 14 publications

Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

Improvement of Radial basis Function Interpolation Performance on Cranial Implant Design

GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal

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