A multiscale and multiblock fuzzy C-means classification method for brain MR images

Yang, Xiaofeng; Fei, Baowei

doi:10.1118/1.3584199

Cited by 58 publications

(37 citation statements)

References 44 publications

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“…We used the Dice similarity measurement (DSM) (Refs. [37][38][39] to compute the overlap ratios. The DSM for each tissue type is computed as a relative index of the overlap between the classification result and the ground truth.…”

Section: Iig2 Classification Evaluationmentioning

confidence: 99%

MR/PET quantification tools: Registration, segmentation, classification, and MR‐based attenuation correction

et al. 2012

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Purpose: Combined MR/PET is a relatively new, hybrid imaging modality. A human MR/PET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MR/PET for brain imaging. Methods:The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MR/PET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with [11 C]PIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6%. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was <6.5%. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MR/PET.

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Section: Iig2 Classification Evaluationmentioning

confidence: 99%

MR/PET quantification tools: Registration, segmentation, classification, and MR‐based attenuation correction

et al. 2012

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“…Enhancing the input matrix by reconstructed signals from detail coefficients in MW-ICA solves this issue to some extent. [20], where each data point belongs to a cluster to some degree, that is, specified by a fuzzy membership grade [23]. Let X = ( 1 , 2 , .…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis

Sindhumol

Kumar²,

Balakrishnan

2013

ISRN Biomedical Imaging

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Multispectral analysis is a potential approach in simultaneous analysis of brain MRI sequences. However, conventional classification methods often fail to yield consistent accuracy in tissue classification and abnormality extraction. Feature extraction methods like Independent Component Analysis (ICA) have been effectively used in recent studies to improve the results. However, these methods were inefficient in identifying less frequently occurred features like small lesions. A new method, Multisignal Wavelet Independent Component Analysis (MW-ICA), is proposed in this work to resolve this issue. First, we applied a multisignal wavelet analysis on input multispectral data. Then, reconstructed signals from detail coefficients were used in conjunction with original input signals to do ICA. Finally, Fuzzy C-Means (FCM) clustering was performed on generated results for visual and quantitative analysis. Reproducibility and accuracy of the classification results from proposed method were evaluated by synthetic and clinical abnormal data. To ensure the positive effect of the new method in classification, we carried out a detailed comparative analysis of reproduced tissues with those from conventional ICA. Reproduced small abnormalities were observed to give good accuracy/Tanimoto Index values, 98.69%/0.89, in clinical analysis. Experimental results recommend MW-ICA as a promising method for improved brain tissue classification.

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“…Fuzzy C-Means (FCM) clustering is an unsupervised classification technique introduced by Bezdek in 1981 [17], where each data point belongs to a cluster with some degree that is specified by a fuzzy membership grade [22]. Let X=(x 1 , x 2 , .., x N ) denotes an input image with 'N' pixels to be partitioned into 'c' clusters.…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

Kannan¹,

Kumar²,

Sindhumol³

2013

IJIGSP

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-In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

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A multiscale and multiblock fuzzy C-means classification method for brain MR images

Abstract: As our classification method did not assume a Gaussian distribution of tissue intensity, it could be used on other image data for tissue classification and quantification. The automatic classification method can provide a useful quantification tool in neuroimaging and other applications.

Cited by 58 publications

References 44 publications

MR/PET quantification tools: Registration, segmentation, classification, and MR‐based attenuation correction

MR/PET quantification tools: Registration, segmentation, classification, and MR‐based attenuation correction

Automated Brain Tissue Classification by Multisignal Wavelet Decomposition and Independent Component Analysis

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

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