The objects in the medical images are not visible due to low contrast and the noise. In general, X-ray, computedtomography (CT), and magnetic resonance imaging (MRI) images are often affected by blurriness, lack of contrast,which are very important for the accuracy of medical diagnosis. It is difficult to segmentation in such case without losingthe details of the objects. The goal of image enhancement is to improve certain details of an image and to improve itsvisual quality. So, image enhancement technology is one of the key procedures in image segmentation for medicalimaging. This article presents a two-stage approach, combining novel and traditional algorithms, for the enhancementand segmentation of images of bones obtained from CT. The first stage is a new combined local and global transformdomain-based image enhancement algorithm. The basic idea of using local alfa-rooting method is to apply it to differentdisjoint blocks of different sizes. We used image enhancement non-reference quality measure for optimization alfarootingparameters. The second stage applies the modified active contour method based on an anisotropic gradient. Thesimulation results of the proposed algorithm are compared with other state-of-the-art segmentation methods, and itssuperiority in the presence of noise and blurred edges on the database of CT images is illustrated.
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