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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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Related in: MedlinePlus

Heat maps for comparative performance analysis of different decomposition levels of wavelet analysis (from left to right: Jaccard index, sensitivity, and specificity).
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pone.0123677.g008: Heat maps for comparative performance analysis of different decomposition levels of wavelet analysis (from left to right: Jaccard index, sensitivity, and specificity).

Mentions: Fig 8 reports the heat maps for comparative performance analysis of different decomposition levels with respect to Jaccard index, sensitivity, and specificity on both BrainWeb and IBSR data sets. From the results reported in Fig 8, it can be seen that the segmentation quality increases with the increase of decomposition level upto 2 irrespective of the segmentation metrics and data sets used. However, the performance of the proposed method detoriates for l ≥ 3. The proposed brain MR image segmentation method for l = 2 performs better than that of other levels in 24, 19, and 20 cases, out of 25 cases each, with respect to Jaccard index, sensitivity, and specificity, respectively. Out of total 75 cases, the proposed segmentation method with l = 2 provides better results in 63 cases, while that with l = 1 and l = 3 attains in only 10 and 2 cases, respectively. Hence, each image is decomposed upto second level without degrading the segmentation quality.


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 different decomposition levels of wavelet analysis (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.g008: Heat maps for comparative performance analysis of different decomposition levels of wavelet analysis (from left to right: Jaccard index, sensitivity, and specificity).
Mentions: Fig 8 reports the heat maps for comparative performance analysis of different decomposition levels with respect to Jaccard index, sensitivity, and specificity on both BrainWeb and IBSR data sets. From the results reported in Fig 8, it can be seen that the segmentation quality increases with the increase of decomposition level upto 2 irrespective of the segmentation metrics and data sets used. However, the performance of the proposed method detoriates for l ≥ 3. The proposed brain MR image segmentation method for l = 2 performs better than that of other levels in 24, 19, and 20 cases, out of 25 cases each, with respect to Jaccard index, sensitivity, and specificity, respectively. Out of total 75 cases, the proposed segmentation method with l = 2 provides better results in 63 cases, while that with l = 1 and l = 3 attains in only 10 and 2 cases, respectively. Hence, each image is decomposed upto second level without degrading the segmentation quality.

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