Computer Vision Pattern Recognition

Theory and applications of CT imaging and analysis by Noriyasu Homma

By Noriyasu Homma

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This way, techniques have been sought, in order to reduce this number after the identification which, in some cases, work as filters before classification. , 1999) presented a methodology for detection of lung nodules with just the pre-processing stages, detection of candidates and classification. Nodule candidates were found by through multiple thresholding and, next, using shape and density attributes and discriminant linear analysis, the classification detected 70% of the nodules indicated by specialists and 3 false positives per slice in average (approximately 80-90 false positives per exam).

M. , de Paiva, A. , Junior, G. B. & Nunes, R. A. (2009). Lung nodules classification in ct images using simpson’s index, geometrical measures and one-class svm, MLDM, pp. 810–822. Informatics and Computerized Tomography Aiding Detection and Diagnosis of Solitary Lung Cancer 35 Silva, E. , Silva, A. , de Paiva, A. , Nunes, R. A. & Gattass, M. (2008). Diagnosis of solitary lung nodules using the local form of ripley’s k function applied to three-dimensional ct data, Computer Methods Programs in Biomedicine.

Introduction High Resolution CT (HRCT) techniques developed in the last decade have become invaluable tools for the detection of subtle diffuse lung disease patterns and for their characterisation into multiple possible diseases. HRCT imaging protocols produce 3D volume data and enable accurate visualisation of imaged anatomy and much better visualisation of the disease patterns than conventional X-rays. However, the amount of information produced by today’s HRCT scanners is beyond the ability of a radiologist to process in normal clinical practice.

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