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Hyperdensity in head CT images has been shown to be a specific feature for diagnosing tuberculous meningitis (TBM) in children. We describe the extraction of hyperdense regions using fuzzy c-means clustering and fuzzy maximum likelihood estimation, thus providing a tool for the enhancement of an often subtle radiological feature. We calculate an asymmetry(More)
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(More)
Computed tomography (CT) is gaining widespread usage as an aid to tuberculous meningitis (TBM) diagnosis 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 various tissue types. The end result is that hyperdense areas of the brain are(More)
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