A dual approach for minimizing false lesion classifications on magnetic resonance images

Bedell, Barry J.; Narayana, Ponnada A.; Wolinsky, Jerry S.

doi:10.1002/mrm.1910370114

Cited by 60 publications

(41 citation statements)

References 29 publications

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“…These false classifications can be reduced to some extent by applying image preprocessing techniques aimed at reducing noise and shading. 13 , 4 However, these steps only partially reduce the false classifications. Elimination of these false classifications requires considerable human intervention.…”

Section: Introductionmentioning

confidence: 99%

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Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

Sajja¹,

Datta²,

He³

et al. 2006

Ann Biomed Eng

Self Cite

111

152

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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.

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Section: Introductionmentioning

confidence: 99%

“…These methods segment images using the feature space generated from the feature vectors comprised of the seed points for different classes. 4 However, nonparametric techniques such as Parzen classifier are also prone to false classifications.…”

Section: Introductionmentioning

confidence: 99%

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

Sajja¹,

Datta²,

He³

et al. 2006

Ann Biomed Eng

Self Cite

111

152

View full text Add to dashboard Cite

show abstract

“…With the inclusion of the regularization terms, Eq. (1) can be written as (3) where N k represents the neighbors of current voxel, and N R is the cardinality of N k . The regularization term can be adjusted by setting the value of α corresponding to neighborhood constraints in Eq.…”

Section: Multi-spectral Adaptive Fcmmentioning

confidence: 99%

“…It is possible to improve the quality of segmentation by combining information from images with multiple contrasts [3,13,16,32]. Feature map-based classification techniques for MR image segmentation have attracted considerable attention because they are fast, simple to implement, and allow expert's input in tissue classification.…”

Section: Introductionmentioning

confidence: 99%

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

Datta

Sajja

et al. 2008

Computerized Medical Imaging and Graphics

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An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the nonspherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

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“…The intensity of a voxel located at the spatial position k (k = 1,..., N, where N is the number of voxels) can be represented as 38 (4) where o k is the observed intensity, t k is the true intensity, G k is the diagonal matrix representing the gain field, and noise (k) is the noise. In multi-spectral case, the focus of the current studies, all the above variables, exceptG k , are vectors.…”

Section: A Multi-spectral Adaptive Fcmmentioning

confidence: 99%

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

He¹,

Sajja²,

Datta³

et al. 2008

Ann Biomed Eng

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Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy cmeans (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

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A dual approach for minimizing false lesion classifications on magnetic resonance images

Cited by 60 publications

References 29 publications

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

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

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

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