Role of correlated noise in textural features extraction. (November 2021)

• Record Type: Journal Article
• Authors: Huerga, Carlos; Morcillo, Ana; Alejo, Luis; Marín, Alberto; Obesso, Alba; Travaglio, Daniela; Bayón, Jose; Rodriguez, David; Coronado, Monica
• Is Part Of: Physica medica. Volume 91(2021)
• Page Start: 87
• Page End: 98
• Publication Date: 2021-11
• Subjects: Texture features -- Radiomic -- Correlated noise -- Wavelet denoising; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.10.015
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Noise texture is a confounding factor in the extraction of the lesion features. Dependence on noise and lesion suggest individual approach for noise influence assessment. Noise estimation enables us to discriminate texture-features associated to noise. Improving the information quality can also improve the predictive model. Abstract: Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the assumption that an improvement in information quality can also enhance the predictive model. A heuristic approach was used that recognizes from the beginning that the noise has its own texture and it was analysed how it affects the quantitative signal data. A simple procedure to obtain noise image estimation is shown; one which makes it possible to extract the noise-texture features at each observation. The distance measured between the textural features in signal and estimated noise images allows us to determine the features affected in each observation by the noise and, for example, to exclude some of them from the model. A demonstration was carried out using synthetic images applying realistic noise models found in medical images. Drawn conclusions were applied to a public cohort of clinical images obtained using FDG-PET to show how the predictive model could be improved. A gain in the area under the receiver operating characteristic curve between 10 and 20% when noise texture information is used was shown. An improvement between 20 and 30% can be appreciated in the estimated model quality.