<title>Segmentation of multiple sclerosis lesions in MRI: an image analysis approach</title>

Abstract

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.

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

“…The concept of using ratio images for lesion segmentation was originally proposed by Krishnan and Atkins. 10 These authors used this technique as a part of the overall segmentation, but have not explicitly used the ratio images for minimization of false positives. In the current studies, removal of false positives, in some instances, resulted in the elimination of true lesions, particularly subtle ones.…”

“…10 A single threshold value, based on careful observation of a number of images, was used for all data sets to obtain a binary ratio mask of brain, excluding CSF and lesions (See Table 1). Negation of this mask was multiplied by the lesions obtained with Parzen classifier.…”

Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks

“…However, our method does not suffer from image data specificity and does not require human interaction to eliminate false positives. Intensity based methods proposed in [5] [6] [8] use mainly the MR image intensity values for classification. As a result they are sensitive to noise and intensity inhomogeneities.…”

“…Some researchers such as [5] [6] [8] have proposed data-driven techniques. These techniques employ image processing procedures such as thresholding, contouring, and region growing.…”

Evaluation of brain atrophy measures in MRI

“…As a preprocessing step, both the PD and the T2 images were normalized, using the initial brain mask as a region of interest for the head images. The voxel intensities within 3 standard deviations from the mean intensity under the brain mask were remapped to the range [0, 255], as described in [7]. To find the CSF from the normalized brain images, we calculated the ratio image PD/T2 and applied a threshold to extract the CSF.…”