Author(s): Charles P. Friedman, October 29, 2013
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Data, Computation, Images
and WaveForms
Prof. Charles P. Friedman
Introduction to Health
Informatics
University of Michigan
October 29, 2013
Where are We?
• Channel 1
• Method Lectures
1. Health information exchange
2. Knowledge representation
3. Information retrieval
4. Imaging and image analysis (today)
5. Policy development and analysis
6. Organization/management
7. Human-computer interaction
And more to follow…
4
Key Questions
1.
What are the primary data types we deal
with in health informatics?
2. How are non-alphanumeric data types
represented and made “computable”?
3. What kinds of computations are
performed on images and how?
4. How are images managed, curated, and
communicated?
We’re going to use simplified examples to
emphasize the methods.
5
Data Types
• Alphanumeric
– Examples: free text, coded text,
numerical results of tests and
observations, others
A patient presents to
emergency department
complaining of flu-like
symptoms. Her fever is
40 C and pulse is 87.
• Images
– Examples: photographs, radiographs
(x-rays), CT scans, ultrasound, others
• Waveforms
– Examples: ECG results, sounds, others
6
Non-Alphanumeric Data
Capture Modalities
• Xrays and Fluoroscopy
• Ultrasound
• Computerized tomography (CT scans and
related)
• Magnetic Resonance
• Electrocardiograms (ECGs)
• Microphones
• Many others…
7
EHRs and Data Types
• Today’s EHR is an alphanumeric EHR
• Images typically are managed in separate
PACS (Picture Archive and Communication
Systems)
– Don’t say: “PACS systems”
• The EHR of the future is a multimedia EHR
that seamlessly integrates data types
Seto B, Friedman C. Moving toward multimedia electronic
health records: how do we get there?
J Am Med Inform Assoc 2012;19:503-505.
8
Key Questions
1.
2.
3.
4.
What are the primary data types we deal
with in health informatics?
How are non-alphanumeric data types
represented and made “computable”?
What kinds of computations are
performed on images and how?
How are images managed, curated, and
communicated?
9
Computability
• In digital computers, ultimately this
requires reduction to binary “bits” (1 or 0)
• Any number can be represented (in Base 2)
as a string of 1’s and 0’s
(1011) Base 2 = (11) Base 10
• Using ASCII codes (a standard), any text
character can be represented as a number
“C” = (67)ASCII = (1000011)Base 2
• “Chuck” = (67|104|117| 95|107) ASCII
10
So How Do We Make Images
and Waveforms Computable?
11
Computable Representation of
Digital Images (2D for now)
• Goal: Make a picture into an array of
numbers
• Method:
– Represent an image as a matrix of dots (pixels)
– Each pixel has a location in the matrix,
corresponding to a location in the image
– Each pixel can be characterized by intensity
and color (if color image)
• Computing on images = mathematical
calculations on pixels
12
2D Image as a Matrix of Pixels
The location, intensity, and color of each pixel completely
represents the image in computable form.
Pixel (10, 10) =
128
13
Image Quality Indices
• Spatial resolution: Number of pixels per
unit area of actual image (pixel density)
– Diagnostic quality digital xray is 2048 x 2048
pixels to cover ~ 200 square inches
• Contrast resolution: Number of bits used
to represent the intensity of a pixel
– “12 bit” monochrome image ~ 4000 shades of
grey
• Temporal resolution: Time required to
generate an image (important for
animation)
14
Representing Waveforms to Make
Them Computable
• Sample the height of the waveform at discrete times.
• As the time interval (sampling interval) diminishes, the
sampled waveform approaches the exact one.
• Analogous to a one-dimensional “image”.
15
Key Questions
1.
2.
3.
4.
What are the primary data types we deal
with in health informatics?
How are non-alphanumeric data types
represented and made “computable”?
What kinds of computations are
performed on images and how?
How are images managed, curated, and
communicated?
16
Computing on Images and
Waveforms
Once images are in“computable” (numerical)
form, what kinds of computations are done
on them?
• Display manipulation
• Image compression
• Computing size and distance
• Computing difference (example in depth)
• Computing structure and automated
inferencing
17
And How Is This Done?
• Straightforward manipulations of
individual pixels
• Creating a mathematical model of the
information in the image or waveform
– Capturing the relationships between pixels
18
Display Manipulation
To help the viewer inspect the image and
detect features of interest.
• Select area of interest
• Zoom in on area of interest
• Enhance brightness
• Enhance contrast
19
Image Compression
To reduce the number of bytes required to
store the image.
• Lossless vs. lossy compression.
• Lossless compression reduces size of image
file without loss of fidelity
• Lossy compression comes with loss of
fidelity but effect may be imperceptible
• Most compression algorithms require a
mathematical model of the image (more
later)
20
Computing Size and Distance
To assist in diagnosis and
treatment planning.
• User points to or outlines
what is to be measured
• Can be computed from pixel
density and physical scale of
the image.
• How big is the lesion?
• What is the distance
between two structures?
21
Computing Difference
• Clinically, a difference between two images
is often hard to detect “by eye”
• By computing on pixels, differences can be
detected
22
How the Difference Algorithm
Works
New Image
Pixel (1 10) =
255
Old Image
Pixel (10, 10)
= 23
Pixel (10, 10)
= 160
Difference Image
Pixel (1 10) =
255
Pixel (1 10) =
0
Pixel (10, 10)
= 137
23
Computing Difference
A patient presents with this X-ray on a follow-up visit
24
Computing Difference
Here was his X-ray three months earlier
25
The “Difference” Image
26
I “Cheated” the Problem of Image
Registration
27
Computing Structure
Advanced computational methods that
enable:
• Automated interpretations of ECGs
• 3 dimensional rendering from 2 dimensional
slices (Visible Human Project)
http://www.youtube.com/watch?v=ojCNUoVfzh4
• Feature detection: automated “grading” of
tumors
• These methods require creation of a
mathematical model of the image
28
Representing Images and
Waveforms Mathematically
• The secret of advanced image and waveform
processing is to create a mathematical model of
the information in the image
• Effectively, this results in a set of equations that:
– Given a location in an image or waveform
– Will return a close approximation to the pixel value
(intensity, color) or the waveform height at that
location
29
Any Musical Tone is a Combination a
Fundamental and Its Harmonics
30
Key Questions
1.
2.
3.
4.
What are the primary data types we deal
with in health informatics?
How are non-alphanumeric data types
represented and made “computable”?
What kinds of computations are
performed on images and how?
How are images managed, curated, and
communicated?
31
How Does One “Retrieve” This
Image?
32
Managing Images Requires a
Standardized Way of Characterizing
Them
• Images bring out the difference between
data and metadata
• The data………………….
• The metadata: Mrs. Jane Smith, Chest Xray, Acquired May 11, 2012
33
The DICOM Header
DICOM = Digital information and Communications in
Medicine
1 SOP Instance UID: Unique identifier for the Study
2 Study Date: Date the Study started, if any previous
procedure steps within the same study have already been
performed.
3 Acquisition Date: The date the acquisition of data that
resulted in sources started.
4 Study Time: The time the acquisition of data that
resulted in sources started.
5 Modality: Type of equipment that originally acquired
the data used to create the images in this Series.
6 Manufacturer: Manufacturer of the equipment that
produced the sources.
7 Institution Name: Institution or organization to which
the identified individual is responsible or accountable.
And six more…
34
Curating and Exchanging Images
• Curating images requires their preservation
– Digital images a big advantage over film
– Storage costs no longer an issue
• Images are a big challenge for information
exchange
– Even compressed images are large files (16 Mb
for a chest xray)
– Images are “bandwidth hogs”
– Need for fluidity of images made the
compelling case for the Next Generation
Internet
35
Summary: Key Questions
1.
2.
3.
4.
What are the primary data types we deal
with in health informatics?
How are non-alphanumeric data types
represented and made “computable”?
What kinds of computations are
performed on images and how?
How are images managed, curated, and
communicated?
36
Image Attributions
•
•
•
“Thymic large cell neuroendocrine carcinoma: report of a resected case - a case report” by PubMed Central is under a
Creative Commons license CC BY 2.0.
“VistA Img” by an employee of the United States Department of Veterans Affairs, taken or made as part of that person's
official duties is in the Public Domain.
“1st thru 5th harmonics of vibrating string” by http://bbasound.wikispaces.com/ is under a Creative Commons license CC BYSA 3.0.
37