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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 obtained by different methods with respect to sensitivity.
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pone.0123677.g010: Heat maps obtained by different methods with respect to sensitivity.

Mentions: Figs 9, 10, and 11 present the heat maps for comparative performance analysis of wavelet based feature extraction and gray level with respect to three quantitative indices, namely, Jaccard index, sensitivity, and specificity. From the results reported in Figs 9, 10, and 11, it can be seen that the performance of the proposed method is better than that of the ℳ1 in most of the cases, irrespective of the input images and quantitative indices used. Out of total 75 cases, the ℳ1 performs better than the proposed method in only 13 cases. The second and third columns of Figs 4 and 5 compare qualitatively the performance of wavelet based analysis and gray level, that is, the proposed and ℳ1 methods. All the results reported in second and third columns of Figs 4 and 5, and first and eleventh columns of heat maps presented in Figs 9, 10, and 11 confirm that the features derived by wavelet transform produce segmented images more promising than do the conventional gray level segmentation. The wavelet analysis provides a multiscale representation that resembles a hierarchical framework for interpreting the image information. At different scales, the details of an image generally characterize different physical structures of the image. In image processing, wavelet decomposition provides information from each scale to be analyzed separately. Hence, the feature extraction scheme based on multiscale analysis has several potential advantages over traditional gray level segmentation.


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

Maji P, Roy S - PLoS ONE (2015)

Heat maps obtained by different methods with respect to sensitivity.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0123677.g010: Heat maps obtained by different methods with respect to sensitivity.
Mentions: Figs 9, 10, and 11 present the heat maps for comparative performance analysis of wavelet based feature extraction and gray level with respect to three quantitative indices, namely, Jaccard index, sensitivity, and specificity. From the results reported in Figs 9, 10, and 11, it can be seen that the performance of the proposed method is better than that of the ℳ1 in most of the cases, irrespective of the input images and quantitative indices used. Out of total 75 cases, the ℳ1 performs better than the proposed method in only 13 cases. The second and third columns of Figs 4 and 5 compare qualitatively the performance of wavelet based analysis and gray level, that is, the proposed and ℳ1 methods. All the results reported in second and third columns of Figs 4 and 5, and first and eleventh columns of heat maps presented in Figs 9, 10, and 11 confirm that the features derived by wavelet transform produce segmented images more promising than do the conventional gray level segmentation. The wavelet analysis provides a multiscale representation that resembles a hierarchical framework for interpreting the image information. At different scales, the details of an image generally characterize different physical structures of the image. In image processing, wavelet decomposition provides information from each scale to be analyzed separately. Hence, the feature extraction scheme based on multiscale analysis has several potential advantages over traditional gray level segmentation.

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