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


Why Computer Vision

Examples

Medical Imaging


Segmentation

Recognition

Trends

Techniques

Tools

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Outline

Background


Why Computer Vision

Examples

Medical Imaging


Segmentation

Recognition

Trends

Techniques

Tools

Summary

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Outline

Background


Why Computer Vision

Examples

Medical Imaging


Segmentation

Recognition

Trends

Techniques

Tools

Summary

References2 of 28


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All types of image processing techniques are collectively known as

digital imaging.

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digital imaging.

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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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Computer Vision is concerned with the theory behind artificialsystems that extract information from images.

Computer Vision can

1. Organize information.

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Computer Vision is concerned with the theory behind artificial

systems that extract information from images.

Computer Vision can


2. Model objects or

environments.

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Computer Vision can


2. Model objects or

environments.

3. Estimate Pose.

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Computer Vision can


2. Model objects or

environments.

3. Estimate Pose.4. Morphometry.

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Computer Vision can


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




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




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




2. Edge extraction.

Delineation of regions by locating their contours.

Extracted using first derivative operators, Laplacian Masks, etc.

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Segmentation




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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