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2552–2558. Hanley, K. S. & Rubins, J. B. (2003). Classifying solitary pulmonary nodules. new imaging methods to distinguish malignant, benign lesions, Postgraduate medicine 114(2): 29–35. , Maruyama, Y. & Watanabe, T. (2000). Growth rate of small lung cancers detected on mass CT screening, The British Journal of Radiology 73: 1252–1259. Hill, M. O. (1973). , Ecology 54: 427–432. , Hoffman, E. A. & Reinhardt, J. M. (2001). Automatic lung segmentation for accurate quantitation of volumetric x-ray ct images, IEEE Transactions on Medical Imaging 20: 490–498.

26 Theory and Applications of CT Imaging and Analysis The first step is the acquisition of the image, which was obtained from a patient’s chest CT exam. Step 2 is the segmentation of the tri-dimensional volume of the nodule using method describe in Section 4. Right after that, the representative features of the nodules are obtained by the use of the Simpson’s Index, that is, the texture analysis stage combined with the geometric features extraction. This index has not been used in applications of analysis of medical images in order to diagnose.

Sonka (eds), Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 5370 of Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, pp. 1–9. , Li, F. & Doi, K. (2008). Computerized detection of lung nodules in thin-section ct images by use of selective enhancement filters and an automated rule-based classifier, Academic radiology 15(2): 165–175. Lillington, G. A. & Caskey, C. I. (2003). Evaluation and management of solitary and multiple pulmonary nodules, Clinics in chest medicine 114: 111–9.

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