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Annual Progress Seminar
Medical Signal & Image Processing
Presented By
PRASHANT BANSODQIP-RESEARCH SCHOLAR
SCHOOL OF BIOSCIENCES & BIOENGINEERINGROLL NO. 05430304
Guided by
PROF. U.B. DESAIELECTRICAL ENGG. DEPARTMENT
INDIAN INSTITUTE OF TECHNOLOGY, POWAI, MUMBAIAugust 2006
Imaging modalities for medical applications
X-ray imaging Nuclear Medicine imaging Computed tomography PET and SPECT MRI imaging Ultrasonography Thermal imaging
Comparison of various Imaging modalities
Characteristics Nuclear Medicine
Ultrasound CT MRI Computed Radiology
2D/3D 2D 2D/3D 3D 3D 3D
Spatial Resolution
Low Moderate Moderate Low High
Contrast Resolution
Low<15 mm
Low<1.5 mm
High<0.1 mm
High<0.1 mm
Low
CNR Low Low High High --
Temporal Resolution
High High Moderate Low Low
No. of Images per study
30 30 + time series
60 100 2
Real time Possible Possible - - -
Radiation Moderate None Moderate None Moderate
Cost Moderate Low High High Moderate
Portability Yes Yes No* No* Some
Distinct Features of Ultrasonography
Safe Non-invasive Easy to use Real time Low cost Comparatively Portable
Applications of Ultrasonography
Obstetric Gynecology Abdominal imaging Echocardiography Superficial structure scanning Vasculature & blood flow Interventional ultrasound Detection, diagnosis & staging of many
diseases
Valvular diseases and assessment of Stenosis Myocardial motion Congenital heart disease Ventricular function Wall thickness Coronary Artery Disease Cardio-myopathy Structural abnormalities Progression of a disease
Diagnostics Applications in Cardiology
Recent Trends in ultrasound imaging
Use of contrast agents High Frequency imaging Tissue motion Intravascular imaging Three & four Dimensional imaging Portable scanners
2D-Imaging: An example
3D-Imaging: An example
Limitations of 2D echo cardiology
Mentally transformation of multiple 2D images to form a 3D impression of the anatomy and pathology.
Such transformation is time-consuming and inefficient.
It is variable and subjective, leading to incorrect decisions in diagnosis.
Conventional 2D ultrasound techniques calculate volume from simple measurements of height, width and length in two orthogonal views, by assuming an idealized shape. This practice can potentially lead to low accuracy, high variability and operator dependency.
Limitations of 2D echo cardiology….
Conventional 2D ultrasound is suboptimal for monitoring therapeutic procedures, or for performing quantitative prospective or follow-up studies, due to the difficulty in adjusting the transducer position so that the 2D image plane is at the same anatomical site and in the same orientation as in a previous examination.
Location and orientation of conventional 2D ultrasound images are determined by the transducer.
Some views are impossible to achieve because of restrictions imposed by the patient’s anatomy or position.
Specific Advantages of 3D & 4D imagingin Cardiology Fast & efficient assessment thus reduction in
acquisition & study time
The C-plane obtained for anatomical views not possible with 2D
Easier for cardiologist to view spatial orientation of heart.
Complete examination through increased perspective from volume data
Better qualitative and quantitative information for diagnosis
Specific Advantages of 3D & 4D imagingin Cardiology
All planes of view reproducible: virtual patient
Simplifies orientation for the referring physician
Better correlation between valves, chambers and vessels.
Volume calculation of heart cavities
Complete analysis possible in one rotation.
Diagnose fetal heart defects in utero, Advance preparation of pathologic conditions for treatment at birth.
Global Cardiac parameters
S.No Parameter Expression01 Left Ventricular Volume
LVVVolume of single shape calculated by integration
02 Left Ventricular VolumeLVM
1.05 x Vm
Vm = Vt (epi) – Vc(endo)
03 Stroke Volume (SV) EDV - ESV04 Ejection Fraction (EF) SV/ EDV x 100 %05 Cardiac Output (CO) SV x HR
EDV= End diastolic volume, ESV=End systolic volumeHR=Heart rate, Vt(epi)= Volume contained in epicardial borders, Vc(endo)= Volume contained within chamber
Regional Cardiac parameters
Wall Thickness Strain Strain rate Shape index Shape spectrum Local stretching Deformation gradient Mean & Gaussian curvature Model specific parameters
Steps for determination of Cardiac parameters
Image Acquisition Preprocessing Registration Segmentation Post processing Visualization Analysis
Research issues
Need for efficient, accurate and robust segmentation of myocardial borders for 2D as well as 3D echo cardiology
Fully automated segmentation
Appropriate model for 3D/4D visualization of heart
3D model based segmentation
Accurate estimation of functional cardiac parameters to assist diagnosis and staging of cardiac disease
Segmentation Difficulties
Tracking of various myocardial boundaries in clinical images which are noisy & blurring.
Transducer motion artifacts modify the brightness
Shape variations are encountered in case of abnormalities
Presence of discontinuities in the acquired images
Inter-observer & intra-observer variability
Existing Methods
Model free techniques- Segment from image by low level processing- Considerable expert intervention- Difficult for real time applications- Suffer from sampling artifacts- Suffer from spatial aliasing and noise- Poor image quality degrades the situation- Difficult for 3D applications
Existing Methods……
Model based approaches- Are based on shape, appearance, surface,
volumetric, deformable model- Suitable for dynamic applications- Can be extended to 3D applications- Accuracy is better due to spatio- temporal
data- High level image processing algorithm
minimize the effect of noise, variability and transducer orientation
Active Appearance Model
This was originally proposed by Cootes. Two steps involved are: Model building and Model matchingModel building consists of generation of statistical models for shape and texture1. Modeling shapex x (1)
denotes mean shape, the shape eigenvector and are shape parameters
2. Modeling texture (2)
denotes mean intensity, the intensity eigenvector and the intensity para
s s
g g
bx s bs
g g b
g g bg
1,
,
metersNow the shape and texture are combined to express components of AAM as functions of model coefficients c:
(3)
(4)
where is a diagonal matrix relating to
s s c s
g c g
s
x x W c
g g c
W
different units of shape & intensity
Active Appearance Model
New shape is generated by eq. 3 by applying similarity transformation to x,y coordinate system of the image
New texture vector is generated by eq.4 and transform to image frame.
AAM is used for segmentation by minimizing the difference between model appearance and target image by gradient descent minimization
Limitations to Active Appearance Model
Disturbances in shape and appearance of objects in images
Large training database is required Training data should be free from
disturbances Computational complexity is more Attempt to enhance robustness further
increases computational complexity
Suggested Modifications
To incorporate a priori shape occurrences possible
To extend the problem for 3D environment for better estimation
To introduce adaptive methods specially suited for ultrasound images
Proposed Work
Algorithm for improvement in AAM & motion models
Possible extension of improved model to 3D data sets
Simulation of electromechanical model of heart
Techniques for mapping image data to electromechanical model for 3D visualization
Estimation of Cardiac functional parameters
Ultimate Aim: 3D visualization & quantification
Thanking you