Texture Analysis of Aggressive and non-Aggressive Lung Tumor CE CT Images

Al-Kadi, O.S and Watson, D. (2008) Texture Analysis of Aggressive and non-Aggressive Lung Tumor CE CT Images. IEEE Transactions on Biomedical Engineering, 55. pp. 1822-1830. ISSN 0018-9294

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This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifyingmalignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluorodeoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure.

Item Type: Article
Keywords: Fractal dimension, lacunarity, texture analysis, tumor aggression.
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QA Mathematics > QA0076 Computer software
Depositing User: Omar Al-Kadi
Date Deposited: 26 Feb 2009
Last Modified: 06 Mar 2017 07:38
URI: http://sro.sussex.ac.uk/id/eprint/1919
Google Scholar:11 Citations

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