Comput. Biol. Med. - Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system.

Tópicos

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Resumo

In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%; the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly.

Resumo Limpo

paper novel method lung nodul detect segment recognit use comput tomographi ct imag present contribut consist sever step first lung area segment activ contour model follow mask techniqu transfer nonisol nodul isol one nodul detect support vector machin svm classifi use effici d stochast d anatom featur contour detect nodul extract activ contour model step solid cavitari nodul accur segment final lung tissu classifi four class name lung wall parenchyma bronchiol nodul classif help us distinguish nodul connect lung wall andor bronchiol attach nodul one cover parenchyma solitari nodul end perform propos method examin compar effici method experi use clinic ct imag two group public dataset lung imag databas consortium lidc anod solid nonsolid cavitari nodul detect overal detect rate number fals posit scan locat detect nodul recogn correct

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