Medical image processing based on multidimensional wavelet transforms - Advantages and trends

Georgieva, Veska; Petrov, Plamen; Zlatareva, Dora

doi:10.1063/5.0041869

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…More specifically, if the XWTC is equal to 1 it occurs a positive correlation; if the XWTC is equal to −1, it occurs a negative correlation [69][70][71]. Therefore, it is useful to remember that wavelet analysis has recently found an increasing number of applications in several fields such as image processing, wavevector spectral analyses of neutron scattering, frequency analyses of meteorological time series, financial data sets, medical image technology (e.g., denoising), and more [72][73][74][75][76][77].…”

Section: Methodsmentioning

confidence: 99%

Experimental Investigation on the Bioprotective Role of Trehalose on Glutamine Solutions by Infrared Spectroscopy

Caccamo

Magazù

2022

Materials

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Glutamine plays a significant role in several basic metabolic processes and is an important regulator of heat shock protein response. The present work is focused on the analysis of the thermal response of aqueous solutions of Glutamine and aqueous solutions of Glutamine in the presence of Trehalose by means of infrared absorption technique. The performed study shows how in the case of a multicomponent system, characterized by a huge number of spectral contributions whose assignment are questionable, the Spectral Distance (SD) and the Cross Wavelet Correlation (XWT) approaches are able to furnish explanatory parameters that can characterize the variations in the spectra behaviour, which is an efficient tool for quantitative comparisons. With this purpose, the analysis has been performed by evaluating the SD and the XWT parameters for the whole investigated spectral range, i.e., 4000–400 cm−1, for scans collected as a function of temperature in the range 20 °C ÷ 60 °C both for Glutamine/Water compounds and for Glutamine /Water/Trehalose mixtures. By means of these analyses, it is found that in aqueous solutions of Glutamine, with respect to aqueous solutions of Glutamine in the presence of Trehalose, the SD and XWT temperature trends follow a linear behaviour where the angular coefficient for Glutamine /Water/Trehalose compounds are lower than that of the Glutamine-Water system in both cases. The obtained findings suggest that Trehalose stabilizes Glutamine against heat treatment.

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Section: Methodsmentioning

confidence: 99%

Experimental Investigation on the Bioprotective Role of Trehalose on Glutamine Solutions by Infrared Spectroscopy

Caccamo

Magazù

2022

Materials

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“…Cheng et al [37] produce a good fusion image of PET/CT, which discover and locates the disease region in the pancreatic gland using wavelet transform by a weighted fusion-based medical image algorithm. Georgieva et al [38] introduce a brief overview of the benefits and current directions of multidimensional wavelet transform-based medical image processing techniques. They also introduce how it might be combined with other approaches that treat each coefficient set as a tensor.…”

Section: Transform Fusion Techniques (Multi-scale Decomposition Methods)mentioning

confidence: 99%

A Brief Analysis of Multimodal Medical Image Fusion Techniques

et al. 2022

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Recently, image fusion has become one of the most promising fields in image processing since it plays an essential role in different applications, such as medical diagnosis and clarification of medical images. Multimodal Medical Image Fusion (MMIF) enhances the quality of medical images by combining two or more medical images from different modalities to obtain an improved fused image that is clearer than the original ones. Choosing the best MMIF technique which produces the best quality is one of the important problems in the assessment of image fusion techniques. In this paper, a complete survey on MMIF techniques is presented, along with medical imaging modalities, medical image fusion steps and levels, and the assessment methodology of MMIF. There are several image modalities, such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Single Photon Emission Computed Tomography (SPECT). Medical image fusion techniques are categorized into six main categories: spatial domain, transform fusion, fuzzy logic, morphological methods, and sparse representation methods. The MMIF levels are pixel-level, feature-level, and decision-level. The fusion quality evaluation metrics can be categorized as subjective/qualitative and objective/quantitative assessment methods. Furthermore, a detailed comparison between obtained results for significant MMIF techniques is also presented to highlight the pros and cons of each fusion technique.

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“…The response at deeper levels of recursion corresponds to information about larger spatial regions of the image, thereby enabling multi-resolution analysis. The success of wavelets has led to their adoption in JPEG-2000 (Usevitch, 2001) and in medical images including chest x-ray (Georgieva et al, 2021). Careful choice of wavelet family grants analysis with some level of sparsity and invariances to rotation or translations.…”

Section: Privacymentioning

confidence: 99%

DeepFixCX: Explainable privacy‐preserving image compression for medical image analysis

Gaudio

Smailagic

Faloutsos

et al. 2023

WIREs Data Min & Knowl

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Explanations of a model's biases or predictions are essential to medical image analysis. Yet, explainable machine learning approaches for medical image analysis are challenged by needs to preserve privacy of patient data, and by current trends in deep learning to use unsustainably large models and large datasets. We propose DeepFixCX for explainable and privacy‐preserving medical image compression that is nimble and performant. We contribute a review of the field and a conceptual framework for simultaneous privacy and explainability via tools of compression. DeepFixCX compresses images without learning by removing or obscuring spatial and edge information. DeepFixCX is ante‐hoc explainable and gives privatized post hoc explanations of spatial and edge bias without accessing the original image. DeepFixCX privatizes images to prevent image reconstruction and mitigate patient re‐identification. DeepFixCX is nimble. Compression can occur on a laptop CPU or GPU to compress and privatize 1700 images per second of size 320 × 320. DeepFixCX enables use of low memory MLP classifiers for vision data; permitting small performance loss gives end‐to‐end MLP performance over 70× faster and batch size over 100× larger. DeepFixCX consistently improves predictive classification performance of a Deep Neural Network (DNN) by 0.02 AUC ROC on Glaucoma and Cervix Type detection datasets, and can improve multi‐label chest x‐ray classification performance in seven of 10 tested settings. In all three datasets, compression to less than 5% of original number of pixels gives matching or improved performance. Our main novelty is to define an explainability versus privacy problem and address it with lossy compression. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Commercial, Legal, and Ethical Issues > Security and Privacy Fundamental Concepts of Data and Knowledge > Big Data Mining

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Medical image processing based on multidimensional wavelet transforms - Advantages and trends

Cited by 9 publications

References 18 publications

Experimental Investigation on the Bioprotective Role of Trehalose on Glutamine Solutions by Infrared Spectroscopy

Experimental Investigation on the Bioprotective Role of Trehalose on Glutamine Solutions by Infrared Spectroscopy

A Brief Analysis of Multimodal Medical Image Fusion Techniques

DeepFixCX: Explainable privacy‐preserving image compression for medical image analysis

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