The aim of this paper was to develop a region based active contour model and Fuzzy C-Means (FCM) technique for segmentation of lung nodules. Ultimately, detection and assisted diagnosis of nodules at earlier stage increase the mortality rate. Among many imaging modalities, Computed Tomography (CT) is being the most sought because of its imaging sensitivity, high resolution and isotropic acquisition in locating the lung lesions. The proposed methodology focuses on acquisition of CT images, reconstruction of lung parenchyma and segmentation of lung nodules. Reconstruction of parenchyma can be employed using selective binary and Gaussian filtering with new signed pressure force function (SBGF-new SPF) and clustering technique was used for nodule segmentation. Comparative experiments demonstrate the advantages of the proposed method in terms of decreased error rate and increased similarity measure. Ó 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
In this paper, a novel feature extraction scheme is proposed, based on multiresolution fast discrete curvelet transform for computer-aided diagnosis of liver diseases. The liver is segmented from CT images using adaptive threshold detection and morphological processing. The suspected tumour region is extracted from the segmented liver using FCM clustering. The textural information obtained from the extracted tumour using Fast Discrete Curvelet Transform (FDCT) is used to train and classify the liver tumour into hemangioma and hepatoma employing artificial neural network classifier. A comparison with a similar algorithm based on Wavelet texture descriptors shows that using FDCT based texture features significantly improves the classification rate of liver tumours from CT scans.
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