Image Analysis and CV in Med - Sonu Iqbal
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Transcript of Image Analysis and CV in Med - Sonu Iqbal
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Image Analysis and Computer Vision in Medicine
Sonu Iqbal
S7 CSE B
Guide: Mr. Anil Jacob
MESCE
November 13, 2010
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Outline
Background
Digital Imaging and Computer Vision
Why Computer Vision
Examples
Medical Imaging
PipelinePreprocessing
Segmentation
Recognition
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Outline
Background
Digital Imaging and Computer Vision
Why Computer Vision
Examples
Medical Imaging
PipelinePreprocessing
Segmentation
Recognition
Trends
Techniques
Tools
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Outline
Background
Digital Imaging and Computer Vision
Why Computer Vision
Examples
Medical Imaging
PipelinePreprocessing
Segmentation
Recognition
Trends
Techniques
Tools
Summary
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Outline
Background
Digital Imaging and Computer Vision
Why Computer Vision
Examples
Medical Imaging
PipelinePreprocessing
Segmentation
Recognition
Trends
Techniques
Tools
Summary
References2 of 28
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Digital Imaging and Computer Vision
All types of image processing techniques are collectively known as
digital imaging.
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Digital Imaging and Computer Vision
All types of image processing techniques are collectively known as
digital imaging.
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Digital Imaging and Computer Vision
All types of image processing techniques are collectively known as
digital imaging.
Computer Vision aims at designing computer systems mimicking the
human sense of sight.
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Why Computer Vision?
Computer Vision is concerned with the theory behind artificialsystems that extract information from images.
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Why Computer Vision?
Computer Vision is concerned with the theory behind artificialsystems that extract information from images.
Computer Vision can
1. Organize information.
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Why Computer Vision?
Computer Vision is concerned with the theory behind artificial
systems that extract information from images.
Computer Vision can
1. Organize information.
2. Model objects or
environments.
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Why Computer Vision?
Computer Vision is concerned with the theory behind artificial
systems that extract information from images.
Computer Vision can
1. Organize information.
2. Model objects or
environments.
3. Estimate Pose.
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Why Computer Vision?
Computer Vision is concerned with the theory behind artificial
systems that extract information from images.
Computer Vision can
1. Organize information.
2. Model objects or
environments.
3. Estimate Pose.4. Morphometry.
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Why Computer Vision?
Computer Vision is concerned with the theory behind artificial
systems that extract information from images.
Computer Vision can
1. Organize information.
2. Model objects or
environments.
3. Estimate Pose.4. Morphometry.
5. Accurate interpretation
and prediction.
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Examples
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Medical Imaging
Technique and process used to create images of the human body to
reveal and diagnose diseases.
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Pipeline
The image analysis and computer vision pipeline is composed of the
following steps:
1. Image Acquisition.
2. Preprocessing.
3. Segmentation.
4. Reconstruction of data.
5. Matching.
6. Recognition.
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Preprocessing
Preprocessing aims at enhancing the image for visualization from:
High-frequency acquisition noise
Background luminance variations
Camera geometrical distorsions.
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Preprocessing
1. Photogrammetric methods
Known spatial or luminance characteristics.
Real time in the display memory for visualization purpose.
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Preprocessing
1. Photogrammetric methods
Known spatial or luminance characteristics.
Real time in the display memory for visualization purpose.
2. Filtering methods
Known spectral characteristics.
Low-pass filters are used for decreasing noise,
Band-pass filters to eliminate periodical perturbations,
High-pass filters to enhance and sharpen edges
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Preprocessing
1. Photogrammetric methods
Known spatial or luminance characteristics.
Real time in the display memory for visualization purpose.
2. Filtering methods
Known spectral characteristics.
Low-pass filters are used for decreasing noise,
Band-pass filters to eliminate periodical perturbations,
High-pass filters to enhance and sharpen edges
3. Geometric corrections
Required when images are geometrically distorted.
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Segmentation
Segmentation involves extracting primitive elements that will then be
approximated by some models.
Isolating regions.
Contours.
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Segmentation
1. Thresholding methods.
Simplest method of image segmentation.
Used to create binary images from grayscale.
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Segmentation
1. Thresholding methods.
Simplest method of image segmentation.
Used to create binary images from grayscale.
2. Edge extraction.
Delineation of regions by locating their contours.
Extracted using first derivative operators, Laplacian Masks, etc.
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Segmentation
1. Thresholding methods.
Simplest method of image segmentation.
Used to create binary images from grayscale.
2. Edge extraction.
Delineation of regions by locating their contours.
Extracted using first derivative operators, Laplacian Masks, etc.3. Region extraction.
Yields parts of the image that satisfy a given uniformity criterion.
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Segmentation
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Recognition
Involves two phases:
1. Learning
Typical characteristics of the objects to be recognized
are determined.
Supervised, the user identifies which are the relevant.
2. Exploitation
Unknown objects are passed through the same pipeline
The object characteristics are measured.
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Recognition
The most classical recognition paradigm is statistical. Techniques
includes:
Neural networks
Advocated as excellent
classifiers. Ability to define
non-linear decision
surfaces. Back-propagation
learning mechanism. Need for large training
sets.
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Trends
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Techniques
Several medical imaging techniques are employed today.
Medical Ultrasonography.
Radiology.
Thermography.
And more.
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Medical Ultrasonography
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Radiology
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Radiology
Brain CT Scan, MRI of Knee
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Thermography
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Thermography
Multiple sclerosis detection. A Dog
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Tomography
Is the basic technique of imaging deployed in above methods. It is the
method of imaging a single plane, or slice.
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Open Source Tools
ImageJ
Java-based image processing program developed at the NationalInstitutes of Health
3DSlicer
Used in a variety of medical applications, including autism,
multiple sclerosis etc. MicroDicom
Free DICOM viewer for Windows. OsiriX
Image processing application dedicated to DICOM images.
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Summary
Is there any motivation for incorporating computer vision algorithms
for physicians?
In order to help the physician interpret, predict and plan.
They provide tools and methods for extracting the necessary pieces
of knowledge.
Recent progress in computer vision has improved new models inmedical imaging.
Fast. Easy. Efficient.
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References
Image Analysis and Computer Vision in Medicine, Thierry Pun,
Guido Gerig and Osman Ratib.
IEEE Transactions on Medical Imaging. Journal. 2008.
Tomographic Reconstruction in the 21st Century, Clackdoyle, R.;
Defrise, M.; IEEE.
Medical Imaging on Wikipedia.
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