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Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Maji P, Roy S - PLoS ONE (2015)

Bottom Line: Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images.The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation.An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India.

ABSTRACT
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

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Heat maps for comparative performance analysis of the proposed method (skull stripping), the method ℳ2 (without skull stripping), and the method ℳ3 (masking using BET) for background separation (from left to right: Jaccard index, sensitivity, and specificity).
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pone.0123677.g012: Heat maps for comparative performance analysis of the proposed method (skull stripping), the method ℳ2 (without skull stripping), and the method ℳ3 (masking using BET) for background separation (from left to right: Jaccard index, sensitivity, and specificity).

Mentions: In the proposed segmentation method, the skull stripping algorithm is used to separate the background from major tissues of brain. To establish the effectiveness of the proposed skull stripping algorithm, the performance of the proposed method is compared with that of the methods ℳ2 and ℳ3. While second, third, and eleventh columns of heat maps presented in Figs 9, 10, and 11 compare the performance of the ℳ2, ℳ3, and the proposed method for three major tissue classes, namely, cerebrospinal fluid (CSF), gray matter, and white matter, Fig 12 presents that of only background region. From the results reported in Figs 9, 10, and 11, it can be seen that the proposed method with proposed skull stripping algorithm performs significantly better than the methods ℳ2 and ℳ3 for segmenting the CSF, gray matter, and white matter. The performance of the proposed method is better than that of ℳ2 in 25, 16, and 22 cases with respect to Jaccard index, sensitivity, and specificity, respectively, while the proposed skull stripping algorithm attains higher performance compared to the BET [41] of ℳ3 in 16, 21, and 13 cases, respectively.


Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Maji P, Roy S - PLoS ONE (2015)

Heat maps for comparative performance analysis of the proposed method (skull stripping), the method ℳ2 (without skull stripping), and the method ℳ3 (masking using BET) for background separation (from left to right: Jaccard index, sensitivity, and specificity).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388859&req=5

pone.0123677.g012: Heat maps for comparative performance analysis of the proposed method (skull stripping), the method ℳ2 (without skull stripping), and the method ℳ3 (masking using BET) for background separation (from left to right: Jaccard index, sensitivity, and specificity).
Mentions: In the proposed segmentation method, the skull stripping algorithm is used to separate the background from major tissues of brain. To establish the effectiveness of the proposed skull stripping algorithm, the performance of the proposed method is compared with that of the methods ℳ2 and ℳ3. While second, third, and eleventh columns of heat maps presented in Figs 9, 10, and 11 compare the performance of the ℳ2, ℳ3, and the proposed method for three major tissue classes, namely, cerebrospinal fluid (CSF), gray matter, and white matter, Fig 12 presents that of only background region. From the results reported in Figs 9, 10, and 11, it can be seen that the proposed method with proposed skull stripping algorithm performs significantly better than the methods ℳ2 and ℳ3 for segmenting the CSF, gray matter, and white matter. The performance of the proposed method is better than that of ℳ2 in 25, 16, and 22 cases with respect to Jaccard index, sensitivity, and specificity, respectively, while the proposed skull stripping algorithm attains higher performance compared to the BET [41] of ℳ3 in 16, 21, and 13 cases, respectively.

Bottom Line: Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images.The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation.An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India.

ABSTRACT
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Show MeSH
Related in: MedlinePlus