INTEGRATION WAVELET-CURVELET DENOISING
AND POST-SEGMENTATION CORRECTION WITH FUZZY C-MEANS
FOR MRI BRAIN TUMOR SEGMENTATION
Dian Pratama PutraP31.2012.01186
SupervisorDr. –Ing. Vincent Suhartono
Romi Satria Wahono, M.Eng.
Research Background• The brain tumors have a particularly complicated
structure (Shen et al. 2005), vary greatly in size, location, shape, internal texture (Resmi 2012), intensities overlapping with normal brain tissue, and often an expanding tumor can deflect and deform nearby brain structures giving an abnormal geometry also for healthy tissue (Cobzas et al. 2007)
• Hence, precise and accurate segmentation of brain tissue is to be a very challenging problem
Research Background (cont..)• Magnetic Resonance Imaging (MRI) is a popular method and
most widely used in medical imaging for clinical diagnosis (Balafar et al. 2010)
• MRI can be adapted to brain image withhigh-contrast, high-spatial resolution and multi-dimensionality (Sikka et al. 2009)
• Segmentation of MRI brain image isquite complicated, difficult and challenging task which needs high-speed, high-accuracy andhigh-precision (Balafar et al. 2010)
Research Background (cont..)• Fuzzy C-Means (FCM) is a very popular clustering
algorithm (Wang et al. 2013)(Balafar et al. 2011) and widely applied to medical problems (Li et al. 2011), particularly in the case of brain tumor segmentation (Gordillo et al. 2013)
• FCM is easy to implement, robust to blurring, applicable to multispectral data and no required assumptions on the probability density function of the data (He et al. 2012)
Research Background (cont..)• But, FCM doesn’t produce a good result
in noisy and inhomogeneity images(Hall et al. 1992)
• The standard FCM very sensitive to noise (He et al. 2012)(Zhao et al. 2013),outliers and other imaging artifacts (Benaichouche et al. 2013) especially in the presence of intensity noisy and inhomogeneity in MRI (Qiu et al. 2013)
Research Background (cont..)• Moreover, the result of brain tumor
segmentation by FCM usually is not enough accurate (Khotanlou et al. 2009) and can generate some classification errors such as misclassification pixel (Benaichouche et al. 2013)
• Standard FCM for MR image segmentation is not efficient by itself
Research Background (cont..)• FCM needs a noise reduction method as
pre-segmentation method and a post-segmentation correction method to refine the segmented image, improve the initial results and produce a more accurate segmentation (Freixenet et al. 2002)
Research Background (cont..)• Wavelet-Transform (WT) and Curvelet-
Transform (CT) is a popular choice noise reduction method (Starck et al. 2002)(Eklund et al. 2013)
• A combined approach exploiting the advantages provided by Wavelet-Curvelet Denoising (WCD) potentially leads to improve performance of MRI noise reduction
Research Background (cont..)• Meanwhile, to refine the potentially
misclassification pixels, used greedy algorithm as post-segmentation correction
• This approach can reallocate misclassification pixels to the most appropriate cluster
Research Background (cont..)• Based on the previous explanation, this
research proposes an integration noise reduction method by Wavelet-Curvelet Denoising (WCD) and post-segmentation correction method by Greedy algorithm with FCM to improve the MRI brain tumor segmentation result
Research Problems (RP)
RP1. Fuzzy C-Means (FCM) segmentation method does not produce a good brain tumor segmentation result due to the noisy MRI images
RP2. The result of MRI brain tumor segmentation by FCM have misclassification pixels
Research Questions (RQ)RQ1. How does WCD-based noise reduction method
affect the accuracy of FCM-based MRI brain tumor segmentation?
RQ2. How does greedy-based algorithm on post-segmentation correction affect the accuracy of FCM-based MRI brain tumor segmentation?
RQ3. How does WCD-based noise reduction method and greedy-based algorithm on post-segmentation correction affect the accuracy of FCM-based MRI brain tumor segmentation?
Research Objectives (RO)RO1. To develop an integration of WCD-based noise
reduction method and FCM for improving MRI brain tumor segmentation accuracy
RO2. To develop an integration of greedy-based algorithm on post-segmentation correction and FCM for improving MRI brain tumor segmentation accuracy
RO3. To develop an integration of WCD-based noise reduction method and greedy-based algorithm on post-segmentation correction with FCM for improving MRI brain tumor segmentation accuracy
Relationship between RP, RQ, ROResearch Problem (RP) Research Question (RQ) Research Question (RQ)
RP1
FCM segmentation method does not produce a good brain tumor segmentation result due to the noisy MRI images
RQ1
How does WCD-based noise reduction method affect the accuracy of FCM-based MRI brain tumor segmentation?
RO1
To develop an integration of WCD-based noise reduction method and FCM for improving MRI brain tumor segmentation accuracy
RP2
The result of MRI brain tumor segmentation by FCM have misclassification pixels
RQ2
How does greedy-based algorithm on post-segmentation correction affect the accuracy of FCM-based MRI brain tumor segmentation?
RO2
To develop an integration of greedy-based algorithm on post-segmentation correction and FCM for improving MRI brain tumor segmentation accuracy
RP1 +
RP2
FCM segmentation method does not produce a good brain tumor segmentation result due to the noisy MRI images, and the segmented result has misclassification pixels
RQ3
How does WCD-based noise reduction method and greedy-based algorithm on post-segmentation correction affect the accuracy of FCM-based MRI brain tumor segmentation?
RO3
To develop an integration of WCD-based noise reduction method and greedy-based algorithm on post-segmentation correction with FCM for improving MRI brain tumor segmentation accuracy
Research Contributions (RC)RC1. An integration of WCD-based noise reduction method
and FCM for MRI brain tumor segmentation(WCD + FCM)
RC2. An integration of FCM and greedy-based algorithm on post-segmentation correction for MRI brain tumor segmentation (FCM + G)
RC3. An integration of WCD-based noise reduction method and greedy-based algorithm on post-segmentation correction with FCM for MRI brain tumor segmentation (WCD + FCM + G)
Related Research1. Sikka et.al (2009) - A Fully Automated Algorithm under
Modified FCM Framework for Improved Brain MR Image Segmentation
2. Forouzanfar et. al (2010) - Parameter Optimization of Improved Fuzzy C-means Clustering Algorithm for Brain MR Image Segmentation
3. Benaichouche et. al. (2013) - Improved Spatial Fuzzy C-means Clustering for Image Segmentation using PSO Initialization, Mahalanobis Distance and Post-segmentation Correction
Related Research (cont..)• Sikka et al. model (2009)
SegmentationModified Fuzzy C-Means (MFCM)
Post-segmentation stepNMAC
MRI Brain DatasetReal : Pancham MRI Center, IndiaSimulated : BrainWeb and Brainsuite2
BiasRemoval
HUM
Contrast stretching
HTRCE
Cluster center estimation
HLPM
Sensitivity (ρ), Specificity (σ)and Similarity index (τ)
Related Research (cont..)• Forouzanfar et al. model (2010)
Paramater optimization
BS (combined GAs + PSO)
Segmentation
Improved Fuzzy C-Means (IFCM)
MRI Brain DatasetSynthetic : Square image with 4
classes intensity valueSimulated : BrainWebReal : IBSR, the Center of Morphometric Analysis
Under segmentation (UnS),Over segmentation (OvS),
Incorrect segmentation (InC), andSimilarity index (SI)
Related Research (cont..)• Benaichouche et al. model (2013)
Pixel classificationPSO Algorithm
SegmentationIFCM with Mahalanobis Distance
Pixel re-classificationGreedy Algorithm
Optimal segmentation accuracy (SA)
MRI Brain DatasetSynthetic: Images containing different
numbers of clusters, types and noises levels
Simulated: BrainWeb
Summary of State-of-the-art on MRI Brain Segmentation
ModelMethod
Dataset Evaluation ResultsPre-processing Segmentation Post-
processingSikkaet al. (2009)
Bias Removal: HUM, Contrast stretching: HTRCE and Cluster center estimation: HLPM
Segmentation using MFCM
Post-processing using NMAC
MRI Real: Pancham MRI Center, IndiaMRI Simulated: BrainWeb & Brainsuite2
Sensitivity (ρ), Specificity (σ) and Similarity index (τ)
WM: ρ = 0.91913; σ = 0.97687; τ = 0.93453
GM: ρ = 0.90833; σ = 0.95201; τ = 0.88426
Forou-zanfaret al. (2010)
Parameter optimization using combined GA and PSO
Segmentation using IFCM
________ MRI Synthetic: Square imageMRI Simulated: Brainweb MRI Real: IBSR
Under-seg, Over-seg, Incorrect-seg, and Similarity index
Simulated: UnS 0.27 %; OvS 3.1 %; InC 0.44 %
Real: UnS 4.2800 %; OvS 20.0522 %; InC 7.1315 %; SI 92.8685 %
Benaichouche et al. (2013)
Pixel classification: PSO
Segmentation using IFCM with Mahalanobis distance
Post-processing correction using Greedy Algorithm
MRI Synthetic: MRI image with different clusters numbers, types, noises levelsMRI Simulated: BrainWeb
Optimal segmentation accuracy (SA)
SA MRI = 93.80 %SA synthetic = 93.11 % (Gaussian), 95.17 % (Uniform), 91.69 % (Salt & Pepper)
Theoretical Framework• Wavelet and Curvelet Transform• Image Segmentation• Fuzzy C-means (FCM) Clustering• Post-segmentation Correction
Theoretical Framework (cont..)
• Wavelet and Curvelet Transform Wavelet and Curvelet transform is used primarily for
smoothing, noise reduction and lossy compression Wavelets are mathematical functions that decompose
data into different frequency components that can be studied with a resolution matched to their scale
Curvelet decompose the image into a set of wavelet bands and analyze each band by local ridgelet transform with different block size for each scale level
Theoretical Framework (cont..)
• Wavelet-based denoising technique includes the following steps:
1. Transform the original image into wavelet domain and acquire the wavelet coefficients
2. Process the wavelet coefficients. Typically involves thresholding the wavelet coefficients to minimize the contribution of noise in the wavelet domain
3. Take inverse wavelet transform on the processed coefficients to produce the denoised image
Theoretical Framework (cont..)
• Curvelet-based denoising technique includes the following steps:
1. Compute all thresholds for curvelets 2. Compute norm of curvelets3. Apply curvelet transform to noisy image4. Apply hard thresholding to the curvelet coefficients5. Apply inverse curvelet transform to the result of
step (4)
Theoretical Framework (cont..)
• Wavelet-Curvelet Combined Approach• Wavelets do not restore long edges with high
fidelity• Curvelets are challenged with small features• Wavelet-Curvelet Denoising (WCD) combined
approach exploiting the advantages provided potentially leads to improve MRI noise reduction performance
Theoretical Framework (cont..)
• Image SegmentationThe principal goal of the segmentation process is to partition an image into regions (also called classes or subsets) that are homogeneous with respect to one or more characteristics, property or features
Theoretical Framework (cont..)
• MRI SegmentationIn the specific case of MRI brain tumors, segmentation consists of separating the different tumor tissues such as solid or active tumor, edema, and necrosis, from normal brain tissues, such as gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF)
Theoretical Framework (cont..)
• Fuzzy C-means (FCM) Clustering• FCM clustering is a very popular clustering algorithm
and widely applied to medical problems, particularly in the case of brain tumor segmentation
• The number of clusters is normally passed as an input parameter
• FCM uses an Euclidean distance measure to assign fuzzy memberships to data element for clustering the data
Theoretical Framework (cont..)
• Post-segmentation Correction• The segmentation algorithm can generate some
classification errors that need to be corrected in order to refine the segmentation
• These errors lead to false contours, local deformations in the natural contours and stray pixels in the homogeneous areas of the image
Theoretical Framework (cont..)
• Post-segmentation CorrectionSteps of potentially misclassification pixels correction:
1. Detection of these pixels in the segmented image by extracting all pixels that do not have the same label in their 3×3 neighborhood ()
2. Reclassification of these extracted pixels using local information in a 5×5 neighborhood () of each extracted pixel in the original image by minimizing homogeneous criterion
Research FrameworkINDICATORS MEASUREMENTSPROPOSED METHOD OBJECTIVE
Sensitivity( ρ )
Specificity( σ )
Segmentation Accuracy
( SA )
Dataset
NITRC and NA-MICMRI Brain image
Pre-processing
Processing
FUZZY C-MEANS
Post-processing
GREEDY ALGORITHM
MODEL ACCURACY
c
WAVELET-CURVELETDENOISING
Research Design
1. Data collection
2. Initial data processing1) Image acquisition2) Ground truth image Processing
3. Proposed model
4. Experiment
5. Evaluation
Data Collection
Initial Data Processing
Proposed Model
Experiment
Evaluation
1. Data Collection• Stack of MRI human brain image• Data set downloaded from :
1. Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC)
2. National Alliance for Medical Image Computing (NA-MIC)
2. Initial Data Processing• Performing data acquisition in image
processing is always the initial step through the workflow sequence, because without an image, no processing is possible
• Initial data processing divided into:1) Image Acquisition2) Ground Truth image processing
3. Image Acquisition
• Data set still in raw form and cannot be used directly
• The raw data files have the extension *.IMG and *.NRRD (Nearly Raw Raster Data)
• Additional tools to perform data-acquisition:a) 3D-Slicer from National Institutes Health (NIH)
b) 3D-Doctor from Able Software Corporation
Ground Truth Image Processing• Decomposing method (ground truth) used to
measure accuracy degree of medical image segmentation results objectively
• Ground truth made manually segmentation using image processing application or hand-labeled by people
4. Proposed Model• Putra’s model
(2014) Noise reduction
Wavelet-Curvelet Denoising (WCD)
SegmentationFuzzy C-Means (FCM)
Pixel re-classificationGreedy Algorithm
MRI Brain Dataset
Simulated : NA-MICReal : NITRC
Segmentation accuracy (SA),Sensitivity (ρ), Specificity (σ)
Compared ModelModel
MethodDataset Evaluation Results
Pre-processing Segmen-tation
Post-processing
Sikkaet al. (2009)
Bias Removal: HUM, Contrast stretching: HTRCE and Cluster center estimation: HLPM
Segmentation using MFCM
Post-processing using NMAC
MRI Real: Pancham MRI Center, IndiaMRI Simulated: BrainWeb & Brainsuite2
Sensitivity (ρ), Specificity (σ) and Similarity index (τ)
WM: ρ = 0.91913; σ = 0.97687; τ = 0.93453GM: ρ = 0.90833; σ = 0.95201; τ = 0.88426
Forou-zanfaret al. (2010)
Parameter optimization using combined GA and PSO
Segmentation using IFCM
________ MRI Synthetic: Square imageMRI Simulated: Brainweb MRI Real: IBSR
Under-seg, Over seg, Incorrect-seg, and Similarity index
Simulated: UnS 0.27%; OvS 3.1%; InC 0.44%Real: UnS 4.2800%; OvS 20.0522 %; InC 7.1315%; SI 92.8685%
Benaichouche et al. (2013)
Pixel classification: PSO Segment-ation using IFCM with Mahalanobis distance
Post-process-ing correction using Greedy Algorithm
MRI Synthetic: MRI image with different clusters numbers, types, noises levelsMRI Simulated: BrainWeb
Optimal segmentation accuracy (SA)
SA MRI = 93.80 %SA synthetic = 93.11% (Gaussian), 95.17% (Uniform), 91.69% (Salt & Pepper)
Putra (2014)
Noise reduction using Wavelet-Curvelet Denoising (WCD)
Segmen-tation using Fuzzy C-Means (FCM)
Post-segmentation correction using Greedy Algorithm
MRI Real:NITRCMRI Simulated: NA-MIC
Segmentation Accuracy (SA), Sensitivity (ρ), Specificity (σ)
?
5. Experiment• Experiment performance using MATLAB ver.R2013a
• Segmentation MRI brain tumor experiment divide:1. Only using FCM segmentation method (FCM) 2. Using FCM plus WCD-based noise reduction method
(WCD + FCM)3. Using FCM plus Greedy-based algorithm on post-
processing (FCM + Greedy)4. Using FCM plus WCD-based noise reduction method
and Greedy-based algorithm on post-processing(WCD + FCM +Greedy)
Evaluation• Quantitative evaluation performance analysis is
based on three figures of merit: 1. Segmentation accuracy
2. Sensitivity
3. Specificity
Research Schedule
No Activity2014, January 2014, February 2014, March
1 2 3 4 1 2 3 4 1 2 3 4
1 Study of Literature
2 Initial Data Processing
3 Designing Proposed Model
4 Experiment and Result
5 Evaluation
6 Thesis Writing
References• S. Shen, W. Sandham, M. Granat, and A. Sterr, “MRI Fuzzy Segmentation of Brain
Tissue Using Neighborhood Attraction With Neural-Network Optimization,” IEEE Trans. Inf. Technol. Biomed., vol. 9, no. 3, pp. 459–467, 2005.
• S. A. Resmi, “A Semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI,” J. Biomed. Sci. Eng., vol. 05, no. 07, pp. 378–383, 2012.
• D. Cobzas, N. Birkbeck, M. Schmidt, M. Jagersand, and A. Murtha, “3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set,” in 2007 IEEE 11th International Conference on Computer Vision, 2007, pp. 1–8.
• M. A. Balafar, A. R. Ramli, M. I. Saripan, and S. Mashohor, “Review of brain MRI image segmentation methods,” Artif. Intell. Rev., vol. 33, no. 3, pp. 261–274, Jan. 2010.
• K. Sikka, N. Sinha, P. K. Singh, and A. K. Mishra, “A fully automated algorithm under modified FCM framework for improved brain MR image segmentation.,” Magn. Reson. Imaging, vol. 27, no. 7, pp. 994–1004, Sep. 2009.
References• A. Eklund, P. Dufort, D. Forsberg, and S. M. Laconte, “Medical image processing
on the GPU - Past, present and future.,” Med. Image Anal., vol. 17, no. 8, pp. 1073–1094, Jun. 2013.
• M. Forouzanfar, N. Forghani, and M. Teshnehlab, “Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation,” Eng. Appl. Artif. Intell., vol. 23, no. 2, pp. 160–168, Mar. 2010.
• Z. Wang, Q. Song, Y. C. Soh, and K. Sim, “An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation,” Comput. Vis. Image Underst., vol. 117, no. 10, pp. 1412–1420, Oct. 2013.
• M. A. Balafar, A. Ramli, and S. Mashohor, “Brain magnetic resonance image segmentation using novel improvement for expectation maximizing,” Neurosciences, vol. 16, no. 3, pp. 242–247, 2011.
• B. N. Li, C. K. Chui, S. Chang, and S. H. Ong, “Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation.,” Comput. Biol. Med., vol. 41, no. 1, pp. 1–10, Jan. 2011.
References• H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, “3D brain tumor segmentation in MRI
using fuzzy classification, symmetry analysis and spatially constrained deformable models,” Fuzzy Sets Syst., vol. 160, no. 10, pp. 1457–1473, May 2009.
• L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek, “A Comparison of Neural Network and Fuzzy C-Means in Segmenting Magnetic Resonance Imaging of the Brain,” IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 672–682, 1992.
• F. Zhao, L. Jiao, and H. Liu, “Kernel generalized fuzzy c-means clustering with spatial information for image segmentation,” Digit. Signal Process., vol. 23, no. 1, pp. 184–199, Jan. 2013.
• A. N. Benaichouche, H. Oulhadj, and P. Siarry, “Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction,” Digit. Signal Process., vol. 23, no. 5, pp. 1390–1400, Sep. 2013.
• I. N. Bankman, “Segmentation,” in in Handbook of Medical Image Processing and Analysis, 2008, pp. 71–72.
References• A. Le Pogam, H. Hanzouli, M. Hatt, C. Cheze Le Rest, and D. Visvikis, “Denoising of
PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation.,” Med. Image Anal., vol. 17, no. 8, pp. 877–91, Dec. 2013.
• J. Starck, E. J. Candès, and D. L. Donoho, “The Curvelet Transform for Image Denoising,” IEEE Trans. image Process., vol. 11, no. 6, pp. 670–684, 2002.
• J. Starck, D. L. Donoho, and E. J. Candes, “Very High Quality Image Restoration by Combining Wavelets and Curvelets,” in SPIE Conference on Signal and Image Processing: Wavelet Applications in Signal and Image Processing, 2001, pp. 9–19.
• A. Bovik, The Essential Guide to Image Processing. Elsevier Inc., 2009.
• C. Qiu, J. Xiao, L. Yu, L. Han, and M. N. Iqbal, “A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation,” Pattern Recognit. Lett., vol. 34, no. 12, pp. 1329–1338, Sep. 2013.
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