Fast, accurate, and reproducible volume estimation is vital to the diagnosis, treatment, and evaluation of many medical situations. We present the development and application of a semi-automatic method for estimating volumes of normal and abnormal brain tissues from computed tomography images. This method does not require manual drawing of the tissue boundaries. It is therefore expected to be faster and more reproducible than conventional methods. The steps of the new method are as follows. (1) The intracranial brain volume is segmented from the skull and background using thresholding and morphological operations. (2) The additive noise is suppressed (the image is restored) using a non-linear edge-preserving filter which preserves partial volume information on average. (3) The histogram of the resulting low-noise image is generated and the dominant peak is removed from it using a Gaussian model. (4) Minima and maxima of the resulting histogram are identified and using a minimum error criterion, the brain is segmented into the normal tissues (white matter and gray matter), cerebrospinal fluid, and lesions, if present. (5) Previous steps are repeated for each slice through the brain and the volume of each tissue type is estimated from the results. Details and significance of each step are explained. Experimental results using a simulation, a phantom, and selected clinical cases are presented.