Lecture 1. - MyCourses

Medical Image Analysis
Welcome to the course!
Lecturers
Mika Pollari: Aalto, NBE, Biomedical Image Processing
Email:
mika.pollari@aalto.fi
Room: Rakentajanaukio 2C, F260
Jyrki Lötjönen / VTT / Combinostics
Email: jyrki.lotjonen@vtt.fi
Course Material
• Electronic Book:
• Klaus D. Toennies: ”Guide To Medical Image Analysis”
• Free access through campus network
• Lecture slides (will be made available)
• Exercise papers
Course Arrangements
• 9 lectures (2—3 h) (in total 22 hours)
• 4 exercise sessions on worked problems
• Not mandatory, no extra points, highly recommended
• 3 assignments on image processing operations
• Grading: pass/fail . Return to Mika Pollari as a PDF
• Options for passing the course:
• completing assignments and exam - grade based on exam
Lecture Schedule
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2.
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6.
7.
8.
9.
Introduction to Medical Image Analysis
Image Enhancement
Region-Based Segmentation (3h)
Edge-Based Segmentation (3h)
Image Features
Feature-Based Segmentation (3 h)
Registration I (3 h)
Registration II
Validation
(today)
(Sept. 15th)
(Sept. 22nd)
(Sept. 29th)
(Oct. 6th)
(Oct. 13th)
(Oct. 27th)
(Nov. 3rd)
(Nov. 10th)
Exercise Schedule
• Four Fridays at 12:15 – 14:00
1.
2.
3.
4.
Exercise 1:
Exercise 2:
Exercise 3:
Exercise 4:
Sep 18
Oct 2
Oct 30
Nov 13
• Location: Otakaari 3A, Lecture room 2
Assignments
• Schedule
• Part 1:
• Part 2
• Part 3
Deadline
Deadline
Deadline
Oct 6
Nov 3
Dec 1
• Assignement will be published 3 week before the deadline
• Written report (PDF-format) is send to mika.pollari@aalto.fi
• You must pass all assignments
Any Questions about the course arrangments?
Introduction to Medical
Image Analysis
NBE-E4010 Medical Image Analysis
Lecture I, Sept 8th 2015
Mika Pollari, mika.pollari@aalto.fi
Contents and Goals for the Lecture
• What is medical image analysis?
• What is a digital image?
• How do we acquire medical images?
• Goal: Demystifying above issues
Medical Image Analysis
What is medical image analysis?
Why Do We Need Medical Image Analysis
• Visual sense is our primary sense
• Computer can ”see” changes which are difficult for human visual
system
• Digital revolution in medical imaging
• Amount of image data e.g. 800 000 studies in HUS röntgen 2008
• Increase in image accuracy, quality and information content
• Human observer ”the last analog component” is a kind of bottleneck
Goals for Medical Image Analysis
• Improvement of image information for human operator (e.g.
radiology)
• Extracting information which the human visual system cannot easily
detect.
• Extracting information for automated machine analysis
The continuum of image processing / analysis
Low Level Process
Mid Level Process
High Level Process
Input: Image
Input: Image
Input: Attributes
Output: Image
Output: Attribute
Output: Understanding
Example: Noise removal
Example: Segmentation
Example: Treatment planning
Where is the tumor?
Planning Application for Radio-Frequency
Ablation
Planning application for RFA treatment
Other High-Level Applications
• Computer Aided Detection
• Computer Aided Diagnosis
• Computer Assisted Surgery
• Computer Aided Treatment Planning
Digital Image
What is a digital image?
Digital Image (2D, gray-level)
• An image is two-dimensional function f(x,y),where (x,y) are the spatial
coordinates and the aplitude of the function f is called intensity
• If (x, y, f) are all finite, discrete quantities, image is a digital image
Image Sampling and Quantisation
• Discretizing the spatial or temporal space is called sampling.
• Discreticing the signal amplitude is called quantization
Pixel
• Pixel (picture element) holds a quantified value of the signal
amplitude in that (sampled) grid location.
• Pixel depth is a number of bits used to store the value of the pixel
• 8-bit (unsigned char 0-255) or (char -128 – + 127)
• 16-bit (short int -32768 and 32767)
• Photometric interpretation specifies how the pixel data should be
interpreted mono- or multi-channel image
• Eg. Pixel depth 32-bit can be interpered multiple ways: mono-channel image
using floating point precision (32 bits) to store the intensity or 4-channel
(R,G,B,A) image where 8-bit is reserved for each channel
Image Matrix/Array Representation
• Pixels are organized in an ordered rectangular array. The pixel array is
a matrix of M columns and N rows.
Display
• During display numerical values are converted to brightness (graylevels)
Dimensionality (spatial)
f(x,y)
f(x,y,z)
Dimensionality (temporal)
TIME
f(x,y,z,t)
Example from temporal data
"Cardiac magnetic resonance Arrhythmogenic right ventricular dysplasia" by Jccmoon at en.wikipedia. Licensed under CC BY 3.0 via Commons https://commons.wikimedia.org/wiki/File:Cardiac_magnetic_resonance_Arrhythmogenic_right_ventricular_dysplasia.gif#/media/File:Cardiac_magnetic_resonance_A
rrhythmogenic_right_ventricular_dysplasia.gif
Pixel Data
• Size of the image Data:
• Rows x Cols x Slices x Time frames x Pixel depth (in bytes)
• Example normal size 3-D CT image
• Dimensions [512, 512, 120, 1 ]
• Pixel depth 16-bit (2 bytes)
• Image size 60 MB
• When image is processed we used arrays as a data structure - In disk
image is just a sequence of bytes.
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0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 1 .
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Meta data
• Image data can not be correctly loaded or understood without meta
data – data about the data.
• Meta data can be internal (a part of the image file) or external.
Internal meta data is called a header.
• Header includes at least following information (All dimensions, pixel
depth, photometric interpretation and spatial resolution)
• Additional information depending image format may include
• Scanner and patient coordinate systems
• Image acquisition details
• Patient details
Image File Formats
• Image file formats provide a standardized way to store the
information describing an image in a computer file.
• Image
= Image Header + Pixel Data (raw image)
Image Size = Header Size + Pixel Data Size
… 0 1 0 H D R 1 1 … ….
1
1
R
A
W
0
0
1
1
• Most of the differences in image file formats is related to image
header
Medical Image Formats
• Two categories – with different goals:
• Aim to standardize images for diagnostic (DICOM)
• Aim to facilate the efficient post-processing (Nifti, Analyze, Minc, Nrrd)
• Digital Imaging and Communications in Medicine (DICOM) is an
imaging and communication standard.
• Goal is that each image is self-explanatory
• A lot of meta-data included
• Organized differently: Study -> Sequences->Images
• Tagged format: tag, the length of the element and the element value
• Includes both mandatory and optional fields
Dicom Example
Medical Image Acquisition
How do we acquire medical images?
Medical Imaging - Overview
• Imaging methods can be categorized several ways:
• Planar versus Tomography
• Planar images are 2D projections of 3D object - structures are superimposed.
• Tomogrphic images ”slice” the data into 2D slices – 3D information can be preserved if
slices cover the 3D domain.
• Structural versus Functional
• Structural images show the anatomy of the object
• Functional images show the activity i.e. metabolic activity of tissues
• According to wave length
Major Imaging Modalities
• Medical Imaging Studies at HUS Röntgen (2008)
• 31 locations in Helsinki and Uusimaa
• About 800 000 studies
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540 000 native studies (2D x-ray)
110 000 sonograms (ultrasound)
80 000 CT scans (computed tomography)
40 000 MRI scans (magnetic resonance imaging)
30 000 Other
X-ray imaging (Planar imaging)
S
D
e
t
e
c
t
o
r
Related planar techniques
• Fluoroscopy – real time constant imaging with a lower dose rate
• Used in image-guided operations
• Angiography – fluoroscopic imaging with contrast medium injected
into the blood vessels
• Digital Substraction Angiography – Digitally substract the angiography
image before contrast agent injection from the contrast enhanced
image.
• Mammography – X-ray imaging of the breast. Filtered
(monochromatic, low-energy) x-ray beams producing high contrast,
high resolution but also higher dose
Tomography imaging
Sectioning body to thin slices with use of any kind of waves.
Tomography Imaging
• Tomographic Imaging requires a reconstruction algorithm, which
transforms measured data (e.g. sinogram) into the image format.
• Medically relevant tomography techniques
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•
•
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Computed Tomography (x-ray)
Magnetic Resonance Imaging (radio-frequency waves)
Positron emission tomography (gamma ray pair from positron annihilation)
Single-photon emission computed tomography (gamma ray)
Computed Tomography
• Most common tomography imaging method
• High spatial accuracy pixel ~ 0.5 mm
• Good contrast for dense tissues and poor
contrast to soft tissues
• Only axial slices can be imaged – artificially
other slice directions can be reconstructed.
• High radiation dose
CT step 1 – scanning
Modern CT scanners have:
~ 900 detectors per row
~ 1000 – 20000 projections per rotation
CT step 2 - image reconstruction
U
N
Image Reconstruction
K
CT reconstruction techniques
• Most common techniques
• Algebraic reconstruction
• Filtered Back Projection
• Simplified Filtered Back Projection Example on White Board
• You can read more on Course Book, Chap 2
Magnetic Resonance Imaging (MRI)
Common tomography imaging method
Slow imaging
Expensive
Good spatial accuracy ~ 1.0 mm
Good contrast to soft tissues and poor contrast to
bones
• Slices can be acquired in any direction
• No radiation dose
• Safety considerations Pace-makers, implants,
etc…
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•
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•
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Several different MR techniques
T1WI
BOLD
T2W2
MRA
PDWI
MRV
DWI
STIR
ADC
Volumetric Images
GE
MR arthrograms
Prefusion images
FLAIR
fMRI
Etc……
MRI
• Principles of MR imaging briefly on whiteboard
Summary – Medical Image Analysis
Low Level Process
Mid Level Process
High Level Process
Input: Image
Input: Image
Input: Attributes
Output: Image
Output: Attribute
Output: Understanding
Example: Noise removal
Example: Segmentation
Example: Treatment planning
Summary – Digital Images
… 0 1 0 H D R 1 1 … ….
1
1
R
A
W
0
0
1
1
Summary – Medical Image Acquisation
SCAN
Reconstruct
Thank you for the attention!
Next Lecture, next Tuesday Sept 15th 14:15 – 16:00