Fractal-Radiomics as Complexity Analysis of CT and MRI Cancer Images

Barucci, Andrea; Farnesi, Daniele; Ratto, Fulvio; Pelli, S.; Pini, Roberto; Carpi, Roberto; Esposito, Marco; Olmastroni, M.; Romei, Chiara; Taliani, Alessandro; Materassi, Massimo

doi:10.1109/compeng.2018.8536249

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…A lot of different radiomics features are continuously introduced, and an exhaustive review is almost impossible. However, some of them deserve a mention, such as fractals and SUV metric for PET specific applications [31,32].…”

Section: Pre-processing Technique Effect On Imagementioning

confidence: 99%

A deep look into radiomics

et al. 2021

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Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

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Section: Pre-processing Technique Effect On Imagementioning

confidence: 99%

A deep look into radiomics

et al. 2021

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“…Structural methods involve techniques of decomposing an image into basic units and then identifying the rules required to construct that given image from its basic units. For instance, Fractal Dimension (FD) is a metric that evaluates image complexity by quantifying how changes in image scale affect image detail ( 9 , 43 , 44 ). FD uses self-repeating structural patterns in order to quantitatively assess the homogeneity of the region of interest, and increases with greater geometric complexity ( 35 , 43 , 45 , 46 ).…”

Section: Approaches To Spatial Assessmentmentioning

confidence: 99%

Spatial assessments in texture analysis: what the radiologist needs to know

Varghese,

Fields,

Hwang

et al. 2023

Front. Radiol.

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To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.

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“…The reported investigations worth mentioning refer to fractal analysis employed to process voice signals, 8 Auditory Brainstem Responses (s-ABRs), 9 electroencephalography (EEG) signals, 10 electromyography (EMG) signals, 11 electrocardiography (ECG) signals, 12 respiratory signals, 13 eye movement, 14 human DNA, 15 magneto-encephalography (MEG) signals, 16 galvanic skin responses (GSRs), 17 computer tomography (CT), 18 and magnetic resonance imaging (MRI). 19 On the other hand, information theory is a useful approach to analyze the changes within time series and patterns. Shannon's entropy, as an indicator of the informational content, is widely used to quantify the amount of information in different systems.…”

Section: Introductionmentioning

confidence: 99%

Complexity-Based Classification of the Coronavirus Disease (Covid-19)

Namazi

Kulish

2020

Fractals

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COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon’s entropy and fractal theory. Shannon’s entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

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Fractal-Radiomics as Complexity Analysis of CT and MRI Cancer Images

Cited by 6 publications

References 21 publications

A deep look into radiomics

A deep look into radiomics

Spatial assessments in texture analysis: what the radiologist needs to know

Complexity-Based Classification of the Coronavirus Disease (Covid-19)

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