Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

Abstract

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)-and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations and ratio maps of PD and T2 weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field -expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under and correctestimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low-lesion load and 93% of the lesions in those cases with high lesion load.

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

“…Only 14 papers included the lesion load of the patients, and the range varied greatly among the papers; the lowest lesion loads ranged from approximately 1 cm 3 (Alfano et al, 2000;García-Lorenzo et al, 2009;Harmouche et al, 2006) to 8 cm 3 (Shiee et al, 2009), and the highest, from 20.0 cm 3 to 130 cm 3 (Alfano et al, 2000). Some authors divided the patients according to lesion load (Khayati et al, 2008a;Sajja et al, 2006), although no consensus exists on how to achieve this division.…”

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images

“…These intensity differences can not be corrected by merely applying the inhomogeneity correction. Therefore, the cerebellar and the posterior fossa regions are very difficult to segment using automatic techniques [29,30,34] and the segmentation of the cerebellum area is usually performed by adopting regional or localized methods [29,30,34]. Thus, it is gratifying that segmentation of the cerebellar region has been considerably improved with GFCM.…”

“…In this case, the segmentation by an expert neurologist (more than 20 years of experience in MRI of MS) was considered as the ground truth. The segmentation by the expert was based on automatic segmentation using the method proposed by Sajja et al [29], followed by manual validation. For the FSE images these four classes primarily represent WM, GM, CSF, and GM+CSF.…”

“…Prior to segmentation, the extrameningeal tissues from the images were removed using a semiautomatic procedure that is described elsewhere [12,29] and these stripped brain images were used as the input to the algorithms. The output of the algorithms included inhomogeneity corrected images, cluster centers, bias field, and memberships of the image volume.…”

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

“…However, both G-K and G-G correctly classified GM and WM (rows 2−4). Based on our own experience and that of a number of other investigators, the cerebellar and the posterior fossa regions are difficult to segment using FCM-based techniques 41,44,53. It is known that on the FSE images the tissue intensities in the cerebellar regions are different from those of the superior parts of the brain 44.…”