From this presentation, you can know about an MRI Tissue Segmentation method called "Multiplicative Intrinsic Component Optimization(MICO)"

1. MRI TISSUE SEGMENTATION
Presentation By:
POOJA G N
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2. Introduction
• MRI : It is a tomographic technique that produces images of internal physical
and chemical characteristics of an object from externally measured nuclear
magnetic resonance (NMR) signals.
• MRI Tissue Segmentation: It is an important technique to differentiate
abnormal and normal tissue in MR image data.
• Need for MRI Tissue Segmentation: MR images are complicated due to the
limitations in the imaging equipment: inhomogeneties in the receiver and large
differences in magnetic susceptibilities of adjacent tissue leads to distortion.
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Intensity inhomogeneity, a major challenge in MRI Tissue Segmentation
• The bias field is also called intensity inhomogeneity.
• It is a low-frequency spatially varying MRI artifact causing a smooth signal
intensity variation within tissue of the same physical properties.
• The bias field arises from spatial inhomogeneity of the magnetic field, variations
in the sensitivity of the reception coil, and the interaction between the magnetic
field and the human body.
• The bias field dependent on the strength of the magnetic field.

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Problems and challenges of brain image segmentation:
• Not all the techniques of image segmentation are suitable for medical image analysis
because of complexity and inaccuracy.
• There is no standard image segmentation technique that can produce satisfactory
results for all imaging applications like brain MRI, brain cancer diagnosis etc.
• Optimal selection of features, tissues, brain and non–brain elements are
considered as main obstacles for brain image segmentation.
• Accurate segmentation over full field of view is another hindrance.
• During the segmentation procedure verification of results is another source of
difficulty.

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MRI Tissue Segmentation Methods
• A HMRF-EM algorithm
• Adaptive Fuzzy C-means algorithm
• Expectation Maximization algorithm
• Coherent Local Intensity Clustering algorithm

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Bias Field Estimation and Energy Minimization Methods
• Maximum Likelihood method
• Adaptive fuzzy C-means algorithm
• Expectation maximization algorithm
• Coherent Local Intensity Clustering (CLIC)
• Fuzzy Logic Gaussian Mixture Model(FLGMM)

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Existing Method
• Simplified Multiplicative Model is used in most state-of-the-art bias
correction methods to represent the bias field nowadays.
• Limitation of this method:
 Even though the model is consistent with the variations arising from the
sensitivity of the receiver coil, the relationship between the measured and
true intensities in MRI is more complicated.
 It cannot be applied for higher intensity magnetic field MRI i.e., for the
fields of strength 5T and 7T.
 This method is not numerically stable in bias field computation.

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Proposed Method
Multiplicative Intrinsic Component Optimization(MICO) :
• This is an energy minimization method which jointly performs bias field estimation
and segmentation of MR images.
• The energy in our formulation is convex in each of its variables, which leads to the
robustness of the proposed energy minimization algorithm.
• This method decomposes the MR images into two multiplicative components, namely,
 the true image that characterizes a physical property of the tissues and
 the bias field that accounts for the intensity inhomogeneity, and their respective
spatial properties.

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Architecture of the proposed method:

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Implementation steps of MICO:
Step 1: initialize u and c.
Step 2: update b as 𝑏.
Step 3: update c as 𝑐.
Step 4: update u as 𝑢.
Step 5: check convergence criterion. If convergence has been reached or the
iteration number exceeds a prescribed maximum number, stop the iteration,
otherwise, go to step 2.

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Advantages of proposed method over existing methods:
• It is robust, accurate and efficient than the current method.
• It can be applied to higher field (e.g. 7 T) MRI scanners, the intensity inhomogeneities
have more complicated profiles than 1.5 T and 3 T MR images.
• It is numerical stable in computation of the bias field.
• This method doesn’t require separate methods for estimating energy and bias field, and
also for segmentation of MR images.

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Results
Fig.1. Original
image
Fig.2. Segmentation
result
Fig.3. Estimated
Bias field
Fig.4. Bias field
Corrected image
Fig.5. Energy decreasing curve

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Conclusion
 The proposed method MICO, for bias field estimation and segmentation of MR
images in a new energy minimization formulation is robust, efficient and accurate
than the existing method. This method has been successfully applied to 1.5T and
3T MR images with desirable results.

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Future Work
• The MICO formulation need to be extended to 3D/4D tissue segmentation
with spatial/spatiotemporal regularization.

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References
• Chunming Li, John C.Gore, Christos Davatzikos, ” Multiplicative Intrinsic component
Optimization(MICO) for MRI bias field estimation and tissue segmentation”, Elsevier, Magnetic
Resonance Imaging 32(2014) 913-923.

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