Abstract
The aim of this paper was to develop an active contour model based on a region and a Fuzzy C-Means (FCM) technique for lung nodule segmentation. In the end, the mortality rate is increased by detection and assisted diagnosis of nodules at an earlier stage. Computed tomography (CT) is the most sought after among many imaging modalities because of its image sensitivity, high resolution and isotropic acquisition. The suggested technique focuses on CT image acquisition, lung parenchyma reconstruction and segmentation of lung nodules. Using selective binary and Gaussian filtering with a new signed pressure force function (SBGF-new SPF) and clustering methods for nodule segmentation, parenchyma reconstruction can be used. The benefits of the proposed approach in terms of reduced error rate and improved measure of similarity are demonstrated by comparative experiments.
CT, FCM, SPF, SBGF, SVM, ANN
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