Medical Imaging and Its Objective Quality Assessment: An Introduction

Thanki, Rohit M.; Borra, Surekha; Dey, Nilanjan; Ashour, Amira S.

doi:10.1007/978-3-319-65981-7_1

Cited by 17 publications

(3 citation statements)

References 41 publications

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“…The shape and size of the structuring element significantly affect the final result. In image processing, morphological operations aim to remove these imperfections by considering the image form and structure [ 57 , 58 , 59 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Section: Methodsmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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“…Utilising standard full reference methods for synthetic image evaluation is difficult as while the images may closely resemble the source image distribution, the generator does not have direct access to the distribution and as such does not reproduce any particular image. This precludes the use of standard comparison techniques as even structural or feature similarity metrics are intended for comparison of matched images [50].…”

Section: Image Quality Metricsmentioning

confidence: 99%

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs

Segal

Rubin

et al. 2021

SN COMPUT. SCI.

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Chest X-rays are a vital diagnostic tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled images to drive forward the development of diagnostic tools; however, this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals. Previous work has sought to address these concerns by creating class-specific generative adversarial networks (GANs) that synthesise images to augment training data. These approaches cannot be scaled as they introduce computational trade offs between model size and class number which places fixed limits on the quality that such generates can achieve. We address this concern by introducing latent class optimisation which enables efficient, multi-modal sampling from a GAN and with which we synthesise a large archive of labelled generates. We apply a Progressive Growing GAN (PGGAN) to the task of unsupervised X-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples. We provide an in depth review of the properties of varying pathologies seen on generates as well as an overview of the extent of disease diversity captured by the model. We validate the application of the Fréchet Inception Distance (FID) to measure the quality of X-ray generates and find that they are similar to other high-resolution tasks. We quantify X-ray clinical realism by asking radiologists to distinguish between real and fake scans and find that generates are more likely to be classed as real than by chance, but there is still progress required to achieve true realism. We confirm these findings by evaluating synthetic classification model performance on real scans. We conclude by discussing the limitations of PGGAN generates and how to achieve controllable, realistic generates going forward. We release our source code, model weights, and an archive of labelled generates.

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“…ThePeakSignaltoNoiseRatio(PSNR)(KutterandPetitcolas,1999)andweighedPSNR(wPSNR) (Thanki et al, 2018) are used to measure imperceptibility. The PSNR depends on the Mean SquareError(MSE)whichisanerrorbetweenthecovercolorbiometricimageandwatermarked color biometric image.…”

Section: Performance Measuresmentioning

confidence: 99%

Fragile Watermarking Framework for Tamper Detection of Color Biometric Images

Thanki¹,

Borra

Kothari

2021

International Journal of Digital Crime and Forensics

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Application of fragile watermarking on biometric images stored at a server or cloud ensures proper authentication and tamper detection when access to the servers was shared. In this paper, a hybrid domain fragile watermarking technique for authenticity of color biometric images, using hybridization of various transforms such as discrete cosine transform (DCT), fast discrete curvelet transform (FDCuT), and singular value decomposition (SVD) is proposed. The hybrid transform coefficients are modified according to the scrambled color watermark to obtain watermarked color biometric image. The security of this technique is strengthened with the usage of Arnold scrambling, and by using multiple secret keys. The proposed technique is analyzed on FEI Brazilian face database. The experimental results show that this technique performs better than the existing fragile watermarking techniques.

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Medical Imaging and Its Objective Quality Assessment: An Introduction

Cited by 17 publications

References 41 publications

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs

Fragile Watermarking Framework for Tamper Detection of Color Biometric Images

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