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Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach...
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...
Investigation on novel methods for extracting objects of interest in medical images has been an important and challenging area of research in image analysis. In particular, medical images are highly spatially correlated and subject to fuzzy distribution of pixels, we present in this paper a new algorithm for medical image segmentation with special reference to abdominal aortic aneurysm and degraded...
Computed tomography is used as an aid in the diagnosis of tuberculous meningitis (TBM) for the examination of a number of visual indicators of the disease. We present an algorithm that uses modified fuzzy c-means clustering to segment CT images of the brain into different tissue types. The ventricle/brain ratio is then calculated to measure hydrocephalus, a common sign of TBM in children
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