This paper describes an intensity-based method for the segmentation of multiple sclerosis lesions in dual-echo PD and T2-weighted magnetic resonance brain images. The method consists of two stages: feature extraction and image analysis. For feature extraction, we use a ratio filter transformation on the proton density (PD) and spinspin (T2) data sequences to extract the white matter, cerebrospinal fluid and the lesion features. The one and two dimensional histograms of the features are then analysed to obtain different parameters, which provide the basis for subsequent image analysis operations to detect the multiple sclerosis lesions. In the image analysis stage, the PD images of the volume are first pre-processed to enhance the lesion tissue areas. White matter and cerebrospinal fluid masks are then generated and applied on the enhanced volume to remove non-lesion areas. Segmentation of lesions is performed in two steps: conspicuous lesions are extracted in the first step, followed by the ext raction of the subtle lesions.The method was tested on the data from eight patients with total manual lesion volumes ranging from 200 to 28000 mm3 Each data set had dual echo PD and T2 images. The manual segmentation of the data was done independently by a trained technologist. The automatic segmentation results were analysed by comparisons with the manual segmentation of the same scans, using similarity index and total lesion volume correlation figures. Results show a total volume correlation of 0.972.

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