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The progress of medical imaging technologies, from X-ray radiography, ultrasonic graph to modern age's Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan has helped the advance of the medical technology as well as the improvement of medical care quality all over the world. It is essential to promote our own medical imaging technologies so as to reduce the future overall medical expense...
This paper presents an automated computed tomography brain segmentation approach used to segment intracranial into brain matters and cerebrospinal fluid in order to detect any asymmetry present. Intracranial midline is used as reference axial where left and right segmented regions are subjectively compared. Two-level Otsu multi-thresholding method has been developed and applied to 213 abnormal cases...
In present study attempt has been taken to determine the degree of malignancy of brain tumors using artificial intelligence. The suspicious regions in brain as suggested by the radiologists have been segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for precise extraction of boundary features of the tumor region. As Fourier descriptors introduce a large number of...
The combination of the different approaches for the segmentation of brain images is presented in this paper. The system segments the CT head images into 3 clusters, which are abnormal regions, cerebrospinal fluid (CSF) and brain matter. Firstly we filter out the abnormal regions from the intracranial area by using the decision tree. As for the segmentation of the CSF and brain matter, we employed...
A novel and more effective algorithm used for segmenting pulmonary nodules in thoracic spiral CT images was presented. The algorithm is based on mean shift clustering method and CI (Convergence Index) features, which can represent the multiple Gaussian model of pulmonary nodules both for solid and sub-solid, substantially. The algorithm has the following steps: (1) calculating the CI features of all...
Medical image segmentation is an essential step for most subsequent image analysis tasks. In this paper a hybrid image segmentation algorithm is proposed, which combines the morphological method of watershed and fuzzy c-means (FCM) clustering. A dilation-erosion contrast enhancement approach is used as a preprocessing stage in order to obtain an accurate estimation of the image borders. Then an initial...
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