Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering (original) (raw)
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Fuzzy based segmentation of multiple sclerosis lesions in Magnetic Resonance brain images
2012
Segmentation is an important step for the diagnosis of multiple sclerosis. This paper presents a new approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. At first, Brain image is considered to be three parts, namely the dark, the gray, and the white part. Then, the fuzzy regions of their member functions are determined by maximizing fuzzy entropy through the Genetic algorithm. Finally, MS lesions and CSF areas are determined by applying a localized weighted filter to the Bright and Dark membership images. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with Gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.
Journal of medical signals and sensors, 2011
This paper introduces a novel methodology for the segmentation of brain MS lesions in MRI volumes using a new clustering algorithm named SCPFCM. SCPFCM uses membership, typicality and spatial information to cluster each voxel. The proposed method relies on an initial segmentation of MS lesions in T1-w and T2-w images by applying SCPFCM algorithm, and the T1 image is then used as a mask and is compared with T2 image. The proposed method was applied to 10 clinical MRI datasets. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations.
Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI
Annals of Biomedical Engineering, 2006
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.
Mixture Segmentation of Multispectral MR Brain Images for Multiple Sclerosis
2005
We present a fully automatic mixture model-based tissue classification of multispectral (T 1-and T 2-weighted) magnetic resonance (MR) brain images. Unlike the conventional hard classification with a unique label for each voxel, our method models a mixture to estimate the partial volumes (PV) of multiple tissue types within a voxel. A new Markov random field (MRF) model is proposed to reflect the spatial information of tissue mixtures. A mixture classification algorithm is performed by the maximum a posterior (MAP) criterion, where the expectation maximization (EM) algorithm is utilized to estimate model parameters. The algorithm interleaves segmentation with parameter estimation and improves classification in an iterative manner. The presented method is evaluated by clinical MR image datasets for quantification of brain volumes and multiple sclerosis (MS).
An Automated MRI Segmentation Framework for Brains with Tumors and Multiple Sclerosis Lesions
— Brain tissue is an intricate anatomical structure and hence thorough detection of numerous brain ailments much relies on precise segmentation of three major tissues, viz., cerebro-spinal fluid (CSF), gray matter (GM) and white matter (WM) in MR brain images. This problem has been addressed in literature, but many key open issues still remains to be investigated. As an initial stride in this development, an automated method for segmentation of deformities like atrophy and tumor in brain MR images is developed. The paper next concentrates on segmenting multiple sclerosis (MS) lesions in WM of central nervous system (CNS). A modified algorithm that relies on the histon based fast fuzzy C-means (HFFCM) is developed. In the former, the experimentation is carried out using brain web datasets and in the latter, the datasets used were from the MICCAI grand challenge II workshop for segmenting MS lesions. The results obtained from the proposed algorithms were compared with the existing methods using performance metrics such as specificity, sensitivity, accuracy, relative absolute volume difference (RAVD), average symmetric absolute surface distance (ASASD) etc. It is observed that the results of segmentation accuracies from the proposed methods were very high when compared with the existing methods.
Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images
NeuroImage: Clinical, 2015
The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.
IEEE transactions on nanobioscience, 2017
Multiple sclerosis is one of the most common neurological diseases in young people. This paper dealt with an automatic biomedical aided tool involving volumetric segmentation of Multiple Sclerosis lesions. To meet this challenge, our proposed methodology requires one preliminary cerebral zones segmentation performed using a new Gaussian Mixture Model based on various databases atlases. Afterwards, lesion segmentation begins with the estimation of a lesion map which is then subjected to threshold constraints and refined by a new lesion expansion algorithm. The evaluation was carried out on four clinical databases integrating various clinical cases which had different lesion loads and were presented by a set of MRI modalities at several noise levels. The results compared to those of the existing methods proved excellent cerebral segmentation with Dice averages close to 0.8 and sensitivity and specificity averages greater than 0.9. In addition, depending on the used database, the lesio...
Automated Segmentation of MS Lesions from Multi-channel MR Images
Lecture Notes in Computer Science, 1999
Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.
SEGMENTATION OF MULTIPLE SCLEROSIS LESION IN BRAIN MR IMAGES USING FUZZY C-MEANS
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.
Two channels fuzzy c-means detection of multiple sclerosis lesions in multispectral MR images
2002
Abstract A novel approach to the detection of multiple sclerosis (MS) lesions in T2and PD-weighted MR images is presented. The core of the proposed method is the use of the two channels fuzzy c-means (FCM) segmentation of data, where the classical FCM approach runs, at first, on the two separate spectra. Then, the one-dimensional distributions of the cluster centers obtained by FCM, are composed in the two-dimensional one, which is a-priori imposed to the two-spectra segmentation procedure.