advanced biomedical image processing

1. Advanced biomedical image analysis
(BME-6231)
Selamawit Workalemahu
FECE
Introduction

2. Selamawit Workalemahu
Introduction
What is an image?
• Simple definition :- a visual representation of something
• A signal = function (variable/variables with physical meaning)
 one-dimensional (e.g. dependent on time)
 two-dimensional (e.g. dependent on two co-ordinates in a plane)
 three-dimensional (e.g. describing an object in space)
 higher-dimensional.
• In general an image is a signal and as with all signals they are functions of physical things.

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Introduction
• Image function value = brightness at image points
• Brightness dependent on several factors
 object surface reflectance properties
• surface material, microstructure and marking
 Illumination properties
• provide their own light as in glow in the dark stickers
 object surface orientation with respect to a viewer and light source
 temperature, distance from the observer

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Types of images
Analog image
• Is a continuous image
• Is described by the spatial distribution of
brightness or gray levels that reflect a
distribution of detected energy
• The image can be displayed using a medium
such as paper or film

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Types of images
Analog image
• Black and white images require only one gray level or
intensity variable while color images require multiple
variables like three basic colors, RGB
• When combined together, the RGB intensities can
produce a selected color at a spatial location of the image

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Types of images
Digital images
• A digital image is discrete in both spatial and intensity (gray level) domains
• A discrete spatial location or finite size with a discrete gray level values is called a
pixel
 E.g. an image of 1024 x1024 pixels (discreate locations) may be displayed in
an 8-bit gray level resolution.
This means that each pixel in the image may have any value from 0 to 255 (discrete in terms of
intensity values)
• The pixel dimensions would depend on the spatial sampling

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Major medical image Modalities
• Projection X-ray (Radiography)
• X-ray Computed Tomography (CT)
• Nuclear Medicine (SPECT, PET)
• Ultrasound
• Magnetic Resonance Imaging
• Optical Imaging (Endoscopy)

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Anatomical vs Functional Imaging
Anatomical imaging
• Modalities that show how the human
anatomy / structure looks like
• Eg. X-ray, CT, MRI

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Anatomical vs Functional Imaging
Functional imaging
• Imaging techniques that show how the
human body system works/ functions
• Eg. SPECT, PET, fMRI, CT with contrast

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Need for image processing
• The images produced by equipment's are composed of pixels, to which discrete brightness and
color values are assigned
 Through image processing they can be efficiently processed, evaluated and analyzed and
through compression, stored and made available to many places at the same time through
appropriate communication networks
• It is possible for doctors to see the interior portions of the human body, with extreme clarity, ease
and detail thus facilitating easy detection and diagnosis of various diseases

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Need for image processing
• It helps to improve images for human interpretation.
• Information can be processed and extracted from images for machine interpretation.

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Components of image processing
• Image processing covers signal gathering, image forming, picture processing,
and image display to medical diagnosis based on features extracted from images
• Image processing covers four main areas:
– Image formation
– Visualization
– Analysis of image
– Management of the acquired information

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Components of image processing

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Image processing
• Image enhancement

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Image processing (cont’d)
Image Restoration( e.g., correcting out-focus images)
• Image Restoration is the operation of taking a corrupt/noisy image and
estimating the clean, original image.
• Corruption may come in many forms such as motion blur, noise and camera
misfocus

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Image processing (cont’d)
• Image Compression

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Computer vision
• Computer vision (CV) is the subcategory of artificial
intelligence (AI) that focuses on building and using
digital systems to process, analyze and interpret
visual data.
• The goal of computer vision is to enable computing
devices to correctly identify an object or person in a
digital image and take appropriate action.

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Computer vision

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Computer vision vs. image processing
Computer vision
Digital image processing
• Input and output are images
• Changes the input properties
• Doesn’t interpret an image
• Often the first step of an application
• Input can be an image or a video. The output can
be a label or a bounding box
• Usually it doesn’t changes the input’s properties
• Extract useful information from the input
• Used after image processing step

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Applications of computer vision
Robotic vision
• Application of computer vision in robotics.
 a sophisticated technology that helps a robot, usually an automated robot,
better identify things, navigate, find objects, inspect, and handle parts or bits
before an application is performed
• Some important applications include :
 Autonomous robot navigation
Video demo showing robotic vacuum cleaner
 Inspection and assembly

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Applications of Image processing

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Object recognition

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Autonomous vehicles
Land, underwater, space

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Traffic monitoring
Video demo, showing computer vision used in traffic monitoring

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Face detection

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Facial expression recognition

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Hand gesture recognition

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Human activity recognition

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Why is medical image analysis Special?
• Because of the patient
• Computer Vision:
 Good at detecting irregulars, e.g. on the factory floor
 But no two patients are alike—everyone is “irregular”
• Medicine is war
 Radiology is primarily for investigation/inspection
 Surgeons are the marines
 Life/death decisions made on insufficient information
 Success measured by patient recovery

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Medical applications
Skin cancer detection Diagnosis of breast cancer

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Segmentation
• Labeling every voxel
• Discrete vs. fuzzy
• How good are such labels?
 Gray matter (circuits) vs. white matter (cables).
 Tremendous oversimplification
• Requires a model

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Registration
• Image to Image
 same vs. different imaging modality
 same vs. different patient
 topological variation
• Image to Model
 deformable models
• Model to Model
 matching graphs

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Course objectives
This course will focus on developing deep knowledge of Biomedical image
analysis in image processing operators, image feature extraction and application
• Understand the digital image fundamentals and transforms to enhance the
biomedical images
• Understand and develop algorithms for medical image processing and analysis

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Course outline

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Course outline

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Course outline

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Course outline