This presentation was made for an engineering technical seminar in Biomedical engineering branch.
The presentation consist of working of MRI and method for segmenting the brain tissue..
The content was taken from various papers which are given as references at the end of ppt.
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
This ppt describes the various features, signal processing methods that are commonly applied like wavelet, HHT, FT etc. Hope it helps someone understand better. EEG During mental arithmetic task dataset is used.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
This presentation discusses the basics of Magnetic Resonance Spectroscopy. provides the first step for researchers and medical students who are interested in gaining knowledge in this field.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
fMRI Segmentation Using Echo State Neural NetworkCSCJournals
This research work proposes a new intelligent segmentation technique for functional Magnetic Resonance Imaging (fMRI). It has been implemented using an Echostate Neural Network (ESN). Segmentation is an important process that helps in identifying objects of the image. Existing segmentation methods are not able to exactly segment the complicated profile of the fMRI accurately. Segmentation of every pixel in the fMRI correctly helps in proper location of tumor. The presence of noise and artifacts poses a challenging problem in proper segmentation. The proposed ESN is an estimation method with energy minimization. The estimation property helps in better segmentation of the complicated profile of the fMRI. The performance of the new segmentation method is found to be better with higher peak signal to noise ratio (PSNR) of 61 when compared to the PSNR of the existing back-propagation algorithm (BPA) segmentation method which is 57.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
This presentation discusses the basics of Magnetic Resonance Spectroscopy. provides the first step for researchers and medical students who are interested in gaining knowledge in this field.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
fMRI Segmentation Using Echo State Neural NetworkCSCJournals
This research work proposes a new intelligent segmentation technique for functional Magnetic Resonance Imaging (fMRI). It has been implemented using an Echostate Neural Network (ESN). Segmentation is an important process that helps in identifying objects of the image. Existing segmentation methods are not able to exactly segment the complicated profile of the fMRI accurately. Segmentation of every pixel in the fMRI correctly helps in proper location of tumor. The presence of noise and artifacts poses a challenging problem in proper segmentation. The proposed ESN is an estimation method with energy minimization. The estimation property helps in better segmentation of the complicated profile of the fMRI. The performance of the new segmentation method is found to be better with higher peak signal to noise ratio (PSNR) of 61 when compared to the PSNR of the existing back-propagation algorithm (BPA) segmentation method which is 57.
Image Processing Technique for Brain Abnormality DetectionCSCJournals
Medical imaging is expensive and very much sophisticated because of proprietary software and expert personalities. This paper introduces an inexpensive, user friendly general-purpose image processing tool and visualization program specifically designed in MATLAB to detect much of the brain disorders as early as possible. The application provides clinical and quantitative analysis of medical images. Minute structural difference of brain gradually results in major disorders such as schizophrenia, Epilepsy, inherited speech and language disorder, Alzheimer's dementia etc. Here the main focusing is given to diagnose the disease related to the brain and its psychic nature (Alzheimer’s disease).
Significance of Brain imaging in Psychiatry. Most of the major Psychiatric disorders are associated with statistically significant differences on various Neuroimaging measures, when comparing groups of patients and controls.
ANALYTICAL STUDY OF BRAIN MRI PROTOCOLS, SEQUENCES AND PARAMETERS FOR DETECTI...ijistjournal
Image analysis by segmentation techniques has wide range of utilities in medical science like surgical planning, post- surgical assessment, abnormality detection etc. This paper is focused on various approaches for analysing the abnormalities in brain MR Images by various Brain MRI protocols, sequences and parameters used in numerous segmentation techniques like classification, clustering etc. The use of appropriate protocols, sequences and parameters set up by the referring imaging centre will prevent repeat MR scanning as well as reduce scanner time. Apart from this, current study leads to plan precise treatment for the medical practitioners/radiographers to improve accuracy and precision to detect abnormal tissues.
Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Seg...CSCJournals
In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This paper presents an automated segmentation of brain tumors in computed tomography images (CT) using combination of Wavelet Statistical Texture features (WST) obtained from 2-level Discrete Wavelet Transformed (DWT) low and high frequency sub bands and Wavelet Co-occurrence Texture features (WCT) obtained from two level Discrete Wavelet Transformed (DWT) high frequency sub bands. In the proposed method, the wavelet based optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Comparative studies of texture analysis is performed for the proposed combined wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Classification and evaluation. The combined Wavelet Statistical Texture feature set (WST) and Wavelet Co-occurrence Texture feature (WCT) sets are derived from normal and tumor regions. Feature selection is performed by Genetic Algorithm (GA). These optimal features are used to segment the tumor. An Probabilistic Neural Network (PNN) classifier is employed to evaluate the performance of these features and by comparing the classification results of the PNN classifier with the Feed Forward Neural Network classifier(FFNN).The results of the Probabilistic Neural Network, FFNN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
3. Introduction
Manual segmentation of brain tissue is both challenging and time-consuming
due to of the large number of MRI slices for each patient which composes the
three-dimensional information and also due to intra/inter-observer variability
of manually segmented scans. Thus developing a robust automated brain tissue
segmentation method is an active research field.
However, automated segmentation of brain tissue is still a challenging
problem due to the complexity of the images, differences in tissue intensities,
noise, intensity non-uniformities, partial volume effects or absence of models
of the anatomy that fully capture the possible deformations in each structure.
Brain tissue segmentation from MR images
Need for automatic segmentation
4. Why to detect brain tissues?
Brain tissue is a particularly complex structure, and its segmentation is an
important step for tasks such as
Cortical labeling
Change detection
Visualization in surgical planning.
Introduction
Brain tissue segmentation from MR images
5. Brain tissues
Introduction
Central nervous system (CNS) is the part of the human nervous network
which integrates the coordination and processing of receiving neural
information. CNS is contained by the
brain and the spinal cord and
constituted by two tissue
components: gray matter tissue
(GM), which is the main CNS
element and consists in neuronal
cell bodies; and white matter
tissue (WM), which is the second
CNS component and it is mainly
composed of militated axon
tracts.
Brain tissue segmentation from MR images
7. Introduction
The modalities have different strengths and thus are used under different circumstances.
• CT captures bone with accuracy, and is used for dosage planning in radiation therapy.
• PET, SPECT, and MRS are typically used to provide functional information with MRI
also gaining usage in that domain.
• MRI produces high contrast between soft tissues, and is therefore useful for detecting
lesions in the brain.
MRI scan
CT scan
Brain tissue segmentation from MR images
8. Magnetic Resonance Imaging
How MRI works?
Patient is bathed in a magnetic field 5000 times stronger than the earths.
This field causes some of the body’s nuclei to behave like tiny compasses and line
up
Then the nuclei are hit by pulsing radio
Once the pulses stop the nuclei go back to their state induced by the magnet
The energy now released by the nuclei acts like miniature radio stations giving out a
signal
These radio waves are picked up by a computer where they are translated into an
image.
Brain tissue segmentation from MR images
11. MRI is based on the magnetization properties of atomic nuclei.
A powerful, uniform, external magnetic field is employed to align the protons that are
normally randomly oriented within the water nuclei of the tissue being examined.
This alignment (or magnetization) is next disrupted by introduction of an external Radio
Frequency (RF) energy. The nuclei return to their resting alignment through various
relaxation processes and in so doing emit RF energy.
After a certain period following the initial RF, the emitted signals are measured. Fourier
transformation is used to convert the frequency information contained in the signal from
each location in the imaged plane to corresponding intensity levels, which are then displayed
as shades of gray in a matrix arrangement of pixels. By varying the sequence of RF pulses
applied & collected, different types of images are created.
Repetition Time (TR) is the amount of time between successive pulse sequences
applied to the same slice.
Time to Echo (TE) is the time between the delivery of the RF pulse and the receipt of
the echo signal.
Magnetic Resonance Imaging
Brain tissue segmentation from MR images
12. Tissue can be characterized by two different relaxation times – T1 and T2.
T1 (longitudinal relaxation time) is the time constant which determines the rate at
which excited protons return to equilibrium. It is a measure of the time taken for
spinning protons to realign with the external magnetic field.
T2 (transverse relaxation time) is the time constant which determines the rate at
which excited protons reach equilibrium or go out of phase with each other. It is a
measure of the time taken for spinning protons to lose phase coherence among the
nuclei spinning perpendicular to the main field.
Magnetic Resonance Imaging
Brain tissue segmentation from MR images
13. Magnetic Resonance Imaging
T1 and T2 weighted imaging
T1- and T2-weighted images can be easily differentiated by looking the CSF.
CSF is dark on T1-weighted imaging and bright on T2-weighted imaging
In T1, CSF tissue has the darkest
intensities while WM has the
brightest. On the contrary, in T2
CSF has the brightest intensities
while WM is the darkest. On
both sequences, GM has an
intermediate gray level.
Brain tissue segmentation from MR images
14. Magnetic Resonance Imaging
T1-weighted images are produced by using short TE and TR times. The contrast and
brightness of the image are predominately determined by T1 properties of tissue.
T2-weighted images are produced by using longer TE and TR times.
Fluid Attenuated Inversion Recovery (Flair)
The Flair sequence is similar to a T2-weighted image except
that the TE and TR times are very long. By doing so,
abnormalities remain bright but normal CSF fluid is attenuated
and made dark. This sequence is very sensitive to pathology
and makes the differentiation between CSF and an abnormality
much easier. In FLAIR , WM and GM have an intermediate
grey level and lesions seems brighter.
Brain tissue segmentation from MR images
15. Magnetic Resonance Imaging
Most common MRI Sequences and their Approximate TR and TE times.
Approximate TR and TE time
Brain tissue segmentation from MR images
17. Brain tissue segmentation
Skull stripping
BET brain extraction tool
The brain surface extractor (BSE)
statistical parametric mapping (SPM2)
The above mentioned algorithms are all MATLAB based tools and use T1
weighted images as inputs to extract skull and remove it from the MR image
• BET (Brain Extraction Tool) deletes non-brain tissue from an image of the whole
head. It can also estimate the inner and outer skull surfaces, and outer scalp surface,
if you have good quality T1 and T2 input images.
Brain tissue segmentation from MR images
18. Automated brain segmentation pipelines usually incorporate a preprocessing step by
which image inhomogeneities are removed.
Sources of inhomogeneities have been studied extensively. The artifacts causes have been
divided into two main groups by classifying them as inherent to the same MRI device or
provoked by the same scanned object.
Main causes in first group are especially derived from radio frequency (RF) transmissions
and receptions but also differences in the magnetic field or eddy currents driven by field
gradients.
Cause derivations in the second group are related to the imaged object itself (position,
shape, and orientation of the object inside the magnet) or dielectric properties of the object.
Brain tissue segmentation
Intensity inhomogeneity
Brain tissue segmentation from MR images
19. Brain tissue segmentation
Partial Volume effects
Automatic brain tissue segmentation algorithms
classify the voxels into their possible classes (CSF,
GM and WM). However, one of the most important
problems are the classification of voxels where more
than one tissue is present. This phenomenon is
referred to as partial volume effects (PVE).
PVE blur the intensity distinction between tissue
classes at their border.
For example, a T1 image voxel containing a boundary between CSF and WM can be
misclassified as GM because of the increase in the blur. To solve this we use Partial
volume correction (PVC) method.
Brain tissue segmentation from MR images
20. Brain tissue segmentation
How to evaluate accuracy segmentation?
Quantitative evaluations are commonly based on the comparison between the
segmentation results and a manually expert labeled volume or ground truth.
Usually, intra-inter observer variability is avoided by the utilization of labeled volumes
from more than one expert. Still, this is not a sufficient condition and it is difficult to find a
consensus among experts.
Thus we use an algorithm to find simultaneous truth and performance level
estimation.(STAPLE).
The method take a collection of segments of an image, and compute simultaneously a
probabilistic estimate of the true segmentation and a measure of the performance level
represented by each segmentation.
Brain tissue segmentation from MR images
21. Brain tissue segmentation
Generally we report evaluation based on statistical analysis measures derived from
classification rates with respect to the ground truth such as true positive (TPR), true negative
(TNR), false positive (FPR) and false negative (FNR) rates. In a single tissue classification,
these rates are defined as:
TPR is the percentage of voxels classified as tissue by the method that are labeled as
tissue by the expert.
TNR is the percentage of voxels classified as non-tissue by the method that are labeled
as non-tissue by the expert.
FPR is the percentage of voxels classified as tissue by the method that are labeled as
non-tissue by the expert.
FNR is the percentage of voxels classified as non-tissue by the method that are labeled
as tissue by the expert.
Brain tissue segmentation from MR images
22. Sensitivity or true positive fraction (TPF) is the classifier ability to correctly identify
tissue voxels . It can be defined as:
Similarly, specificity is defined as the classifier ability to identify non-tissue:
The accuracy of the classifier is usually computed as the rate of correct predicted voxels
over all predicted voxels. Hence
Brain tissue segmentation
Brain tissue segmentation from MR images
23. Brain tissue segmentation
Conversely, the error rate of the classifier is given by the misclassified voxels over all
predicted voxels as:
Furthermore, similarity indexes can be used to compute the accuracy of the method.
Dice coefficient is defined as the set agreement between classification and ground truth :
Analogously, the Jaccard similarity index, measures the overlap between the segmentation
results and the ground truth as:
Brain tissue segmentation from MR images
24. Brain tissue segmentation
Other measures based on intensity, distance or connectivity can be are
As we know, Dice coefficient is the most broadly used measure to quantitatively evaluate
the accuracy of brain tissue segmentation. The Fractional Brain Tissue of a given class
returns the normalized fraction of the given tissue in the brain.
It is defined as the amount of voxels which are classified as the given class divided by all
brain voxels. Hence:
Brain tissue segmentation from MR images
25. We measure the atrophy in MS lesion tissues using the Brain parenchyma
factor coefficient which is defined as the number of GM,WM voxels and tissue
lesion voxels L divided by the all the brain voxels. A decrease in BPF over time
might give early diagnostic clues about the onset of MS disease:
Brain tissue segmentation
Diagnosis of MS Lesion
What is Multiple Sclerosis?
Brain tissue segmentation from MR images
26. Brain tissue segmentation
Image morphology provides a way to incorporate neighborhood and distance information.
The basic idea in morphology is to convolve an image with a given mask and to binarize
the result of the convolution using a given function. Choice of convolution mask and
binarization function depend on the particular morphological operator being used.
Binary morphology has been used in several segmentation systems, their functional
description is given on next slide.
Mathematical Morphology
Brain tissue segmentation from MR images
27. Brain tissue segmentation
Mathematical Morphology
Erosion: An erosion operation on an image I containing labels 0 and 1, with a structuring
element S, changes the value of pixel i in I from 1 to 0, if the result of convolving S with I,
centered at i, is less than some predetermined value
Dilation: Dual to erosion, a dilation operation on an image I containing labels 0 and 1, with a
structuring element S, changes the value of pixel i in I from 0 to 1, if the result of convolving S
with I, centered at i, is more than some predetermined value.
Brain tissue segmentation from MR images
28. Brain tissue segmentation
Deformable Models
Deformable Models are used for object recognition.
There are two type of deformable models – Parametric deformable model and
Geometric deformable model
Snakes and Balloons are the two deformable models we will need for brain tissue
segmentation.
Snakes model is a 2 D model which maps the curves.
Balloons model is a 3 D model which is implicit.
Brain tissue segmentation from MR images
30. Brain tissue segmentation
Procedure of Segmenting Brain tissue
1. EM segmentation for correction of gain due to RF coil inhomogeneities in the data
2. binary morphology and connectivity to incorporate topological information, and
3. active contours to add spatial knowledge to the segmentation process.
Brain tissue segmentation from MR images
31. Brain tissue segmentation
EM segmentation
• Expectation Maximization Segmentation (EM) is a Iterative method consisting of
two steps:
Expectation Step: Given the current bias field , E step computes likelihood of each
tissue class.
Maximization Step: Given the likelihood of each tissue class, the M step estimates
the image homogeneities
• EM segmentation does not incorporate any spatial information for the classification
of voxels.
Brain tissue segmentation from MR images
32. N3 is nonparametric non-uniform intensity normalization algorithm used for bias field
correction.
Bias field signal is a low-frequency and very smooth signal that corrupts MRI images
specially those produced by old MRI machines, which makes it difficult for image
segmentation algorithms to produce satisfactory results.
What is Bias field?
Input scan BET Output N3 output Bias field
Brain tissue segmentation
Brain tissue segmentation from MR images
33. Using the model described above, we divide the segmentation of the brain
into three steps.
The first step is to remove the gain artifact from the data and to classify the voxels
into four classes: white matter, grey matter, CSF, and skin (or fat), purely on the basis
of their signal intensities.
Due to natural overlap between intensity distributions of the various structures,
misclassifications are likely at this stage.
In particular, muscle is likely to be classified as gray matter, fat classified as white
matter, and nerve fibers classified as white or gray matter.
Brain tissue segmentation
Brain tissue segmentation from MR images
34. The Second step aims to reduce some of the misclassification by using neighborhood
and connectivity information. It uses morphological operators to "shave off" the nerve
fibers and muscles connecting the brain tissue to the cranium, and then uses
connectivity to find the largest connected component of white and grey matter in the
image.
The strategy is that misclassified fat, muscle, and nerve fibers will get cut off from the
central largest component, which is the brain tissue.
Due to the variation in the size of the connectors from the brain tissue to the cranium,
often the brain tissue is not isolated at the end of this step, which is when we use the
third step.
Brain tissue segmentation
Brain tissue segmentation from MR images
35. The third step uses expert input to annihilate connections between the brain and
spurious structures in a few carefully chosen slices of the data, and then employs
region based deformable contours to propagate the manually drawn contours to the
rest of the volume.
Brain tissue segmentation
Brain tissue segmentation from MR images
Input
Image
Remove
Gain
artifact
Using Morphological
operators
Using
deformable
contours
Output
Image
36. 1.
2. 3.
4. 5. 6.
Step 1 – Iterations of EM segmenter
Brain tissue segmentation
Brain tissue segmentation from MR images
38. Step 2 – Iterations using balloon deformable model
Brain tissue segmentation
Brain tissue segmentation from MR images
39. Brain tissue segmentation
Implementation of the EM segmentation, morphological operations, and connectivity
is done on IBM POWER Visualization Server (PVS).
Time required for each step is given below.
Brain tissue segmentation from MR images
40. Thank You for your time !
All the questions are welcome and appreciated !
Brain tissue segmentation from MR images
41. S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping,
17(3):143-155, November 2002.
Segmentation of brain tissue from magnetic resonance images by Tina Kapur,
Massachusetts Institute of Technology, February1995
MRI brain tissue segmentation by Sergi Valverde, university of Girona ,2012
http://www.brainvoyager.com/bvqx/doc/UsersGuide/Segmentation/IntensityInhomog
eneityCorrection.html
Brain tissue segmentation from MR images
References
Editor's Notes
A typical image acquisition consists of exposing patients to the imaging equipment, sometimes with contrast enhancing agents or markers, and generating an image of their anatomy. This image can be a 2D projection of a 3D scene as is produced with X-ray or ultrasound, or it can be a full 3D image, as generated by CT or MRI.
60Mhz, 10^16 billion particles of hydrogen
Give earth example
Repetition time and time to echo
The 3-dimensionsal image it provides is built up in units called voxels. Each one represents a tidy cube of brain tissue
Parenchyma - the functional tissue of an organ as distinguished from the connective and supporting tissue
Atrophy- (of body tissue or an organ) waste away, especially as a result of the degeneration of cells
Homogeneity- the quality or state of being all the same or all of the same kind.