Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images

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 identifying malignant 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 fluoro deoxyglucose 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.

[1]  Thomas F Hany,et al.  Integrated PET/CT: current applications and future directions. , 2006, Radiology.

[2]  Sumit K. Shah,et al.  Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps. , 2006, European journal of radiology.

[3]  Yung-Chang Chen,et al.  Multi-threshold dimension vector for texture analysis and its application to liver tissue classification , 1993, Pattern Recognit..

[4]  J Konishi,et al.  Contribution of PET in the detection of liver metastases from pancreatic tumours. , 1999, Clinical radiology.

[5]  Christos Faloutsos,et al.  Fast feature selection using fractal dimension , 2010, J. Inf. Data Manag..

[6]  K Kuriyama,et al.  [The usefulness of fractal geometry for the diagnosis of small peripheral lung tumors]. , 1998, Nihon Igaku Hoshasen Gakkai zasshi. Nippon acta radiologica.

[7]  C. Floyd,et al.  Fractal texture analysis in computer-aided diagnosis of solitary pulmonary nodules. , 1997, Academic radiology.

[8]  M D Schnall,et al.  Discrimination of MR images of breast masses with fractal-interpolation function models. , 1999, Academic radiology.

[9]  Chin-Tu Chen,et al.  Fractional box-counting approach to fractal dimension estimation , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[10]  Shoji Kido,et al.  Fractal Analysis of Internal and Peripheral Textures of Small Peripheral Bronchogenic Carcinomas in Thin-section Computed Tomography: Comparison of Bronchioloalveolar Cell Carcinomas With Nonbronchioloalveolar Cell Carcinomas , 2003, Journal of computer assisted tomography.

[11]  James M. Keller,et al.  Texture description and segmentation through fractal geometry , 1989, Comput. Vis. Graph. Image Process..

[12]  Shiva K. Das,et al.  Three-dimensional tumor perfusion reconstruction using fractal interpolation functions , 2001, IEEE Transactions on Biomedical Engineering.

[13]  Nikos Dimitropoulos,et al.  Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers , 2006, Artif. Intell. Medicine.

[14]  Fahima Nekka,et al.  The modified box-counting method: Analysis of some characteristic parameters , 1998, Pattern Recognit..

[15]  Shoji Kido,et al.  Fractal Analysis of Small Peripheral Pulmonary Nodules in Thin-section CT: Evaluation of the Lung-nodule Interfaces , 2002, Journal of computer assisted tomography.

[16]  S. Noma,et al.  Thin-Section CT Features of Intrapulmonary Lymph Nodes , 2001, Journal of computer assisted tomography.

[17]  Nirupam Sarkar,et al.  An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[18]  J. Verdebout,et al.  The role of microvessel density on the survival of patients with lung cancer: a systematic review of the literature with meta-analysis , 2002, British Journal of Cancer.

[19]  Yung-Chang Chen,et al.  Ultrasonic Liver Tissues Classification by Fractal Feature Vector Based on M-band Wavelet Transform , 2001, IEEE Trans. Medical Imaging.

[20]  M. Fox,et al.  Fractal feature analysis and classification in medical imaging. , 1989, IEEE transactions on medical imaging.

[21]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[22]  James M. Keller,et al.  On the Calculation of Fractal Features from Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Maria Petrou,et al.  Image processing - dealing with texture , 2020 .

[24]  Wen-Li Lee,et al.  Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform , 2003, IEEE Transactions on Medical Imaging.

[25]  Alan I. Penn,et al.  Estimating fractal dimension with fractal interpolation function models , 1997, IEEE Transactions on Medical Imaging.

[26]  Ruey-Feng Chang,et al.  Classification of breast ultrasound images using fractal feature. , 2005, Clinical imaging.

[27]  R W Günther,et al.  Classification of lung tumors on chest radiographs by fractal texture analysis. , 1996, Investigative radiology.

[28]  Renuka Uppaluri,et al.  Fractal analysis of high-resolution CT images as a tool for quantification of lung diseases , 1995, Medical Imaging.