Image Management

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Image Management

• Dr. Hayit Greenspan
• Dept of BioMedical Engineering
• Faculty of Engineering
• hayit_at_eng.tau.ac.il
• 640-7398

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Roles for Imaging in Health Care

• Diagnosis
• Assessment and Planning
• Guidance of Procedures
• Communication
• Education and Training
• Research

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Image Diagnosis in Dermatology
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Fetus Ultrasound
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Example of cross-sections through several parts
of the body skull, thorax, and abdomen,obtained
by computed tomography.
Visualization of the values of the attenuation
coefficients by way of gray values produces an
anatomic image.
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Spinal cord
Brain section
MRI Image Diagnosis
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Roles for Imaging in Health Care

• Diagnosis
• Assessment and Planning
• Guidance of Procedures
• Communication
• Education and Training
• Research

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fMRI
A functional map (in color) in the cerebellum
during performance of a cognitive peg- board
puzzle task, overlaid on a T2-weighted axial
image in gray scale. The dentate nuclei appear
as dark crescent shapes at the middle of the
cerebellum due to iron deposits. fMRI images
were acquired by conventional T2-weighted FLASH
techniques with a spatial resolution of
1.25x1.25x8 mm3 and a temporal resolution of 8
seconds. Each color represents a 1 increment,
starting at 1. R, right cerebellum L, left
cerebellum. A left-handed subject used the left
hand to perform the task. Bilateral activation
in the dentate nuclei and cerebellar cortex was
observed. The activated area in the dentate
nuclei during performance of pegboard puzzle was
3-4 times greater than that seen during the
visually guided peg movements. (see details in
Kim et al., 1994b).
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fMRI
Whole brain functional imaging study during a
visuo-motor error detection and correction task.
Functional images were acquired by the
multi-slice single-shot EPI imaging technique
with spatial resolution of 3.1x3.1x5 and
temporal resolution of 3.5 seconds. The skull and
associated muscles were eliminated by image
segmentation. The 3-D image constructed from
multi-slice images was rendered by Voxel View
program (Vital Images, Fairfield, Iowa).The task
was to move a cursor from the central start box
onto a square target by moving a joystick. Eight
targets were arranged circumferentially at 450
angles and displaced radially at 200 around a
central start box. Activation (in color) is
observed at various brain areas. Top image
displays the brain as a 3-D solid object so that
only the cortical surface is seen. In the bottom
image, a posterior section was removed at the
level of the associative visual cortex to display
activation not visible from the surface (Kindly
provided by Jutta Ellermann, Jeol Seagal, and
Timothy Ebner).
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Medical Image Databases

• Medical Images are at the heart of diagnosis,
  therapy and follow-up.
• Digital medical image data in US per year
  
• bytes (petabytes).
• Generation Acquisition
• Post processing Management.
• Medical imaging information types
• still images pictures moving images
  structured text plain text sound graphics.
• Driving the shift toward multimedia applications
  in medical imaging
• market demand capital investment in imaging
  devices need to organize and store multimodal
  image data associated clinical data ability to
  extract info in images.

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Biomedical Imaging
Structural
Functional
MRI
Ultrasound
fMRI
Medical optical imaging
X-ray CT
Microscopy
Projectional x-ray
Emission CT
CR
Mammograph
PET
SPECT
DSA
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Current Information Systems
Originators
Publishers
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Digital Libraries
Originators
Value-added Index Services
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Multimedia Information SystemsWork-centered
Scenario
Databases
Co-workers/ Collaborators
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Visual Information Systems

• Example
• Patient needs neurosurgery to remove a tumor
• CT, MRI, PET scans digitized and scanned
• Images are registered with a 3D brain model
• Locate tumor
• Path planning
• Using tumor as template, request to find
• patients of same sex
• with similar tumors
• in similar positions

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Imaging Informatics

• Information systems and networks that facilitate
  the
• Acquisition
• Storage
• Transmission
• Processing
• Analysis
• Management
• of medical images.
• Imaging Informatics- a new discipline
• Image generation
• Image management
• Image manipulation
• Image integration

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Basic concepts in Image Manipulation

• Global Processing enhance contrast resolution
• Segmentation finding regions of
  interest
• Feature detection extraction
• Classification
• Examples
• Histogram equalization
• Temporal subtraction (DSA)
• Screening
• Quantitation
• 3D reconstruction and visualization
• Multimodality image fusion

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Contrast enhancement
Principle of contrast enhancement (a) intensity
distribution along a line of an image (b) same
distribution after injection of the contrast
medium (c) intensity distribution after
subtraction (d) intensity distribution after
contrast enhancement.
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Example of digital subtraction angiography (DSA)
of the bifurcation of the aorta
An initial image mask is obtained digitized and
stored Contrast medium is injected Number of
images are obtained. Mask is subtracted The
resulting image contains only the relevant
information The differences can be amplified so
the eye will be able to perceive the the blood
vessels. Quality of deteriorate due to
movements of the body can be corrected to some
extent.
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Texture Segmentation of MRI images
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VOXEL-MAN(Hamburg) 3D Visualization
http//www.uke.uni-hamburg.de/institute/imdm/idv/i
ndex.en.html
Atlasas of brain and other organs allow views
from any viewpoint Fusion of modalities
Anatomical atlases
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Video COVIRAComputer Vision in Radiology
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Basic concepts in Image Management

• Digital acquisition of images offers the exciting
  prospect of reducing the physical space
  requirements, material cost, and manual labor of
  traditional film-handling tasks, through online
  digital archiving, rapid retrieval of images via
  querying of image databases, and high-speed
  transmission over communication networks.
• Researchers are working to develop such systems
  that have such capabilities - picture archiving
  and communication systems (PACS).
• Issues that need to be addressed for PACS to be
  practical
• technology for high-resolution acquisition
• high capacity storage
• high-speed networking
• standardization of image-transmission and storage
  formats
• storage management schemes for enormous volumes
  of data
• design of display consoles/workstations

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Evolution of Image Management in PACS

• Early attempts in mid 80s
• Univ. of Kansas, Templeton et al (84) earliest
  prototype systems to study PACS in radiology
• Inst of radiology in St. Louis, Blaine et al
  (83) PACS Workbench
• experiments in image acquisition, transmission,
  archiving and viewing
• Substantial progress on several fronts
• Standards (DICOM) support transition from
  acquisition devices to storage devices
• Expansion in disk capacities and dramatic
  decreases in cost
• Hierarchical storage-management schemes
• Compression methods
• Increased resolution workstation display
• Image manipulation tools
• Many Departments have mini-PACS Large scale PACS
  increased in number from 13 to 23 in a 15-month
  period.

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Image ManagementIndexing Retrieval

• We formed image archives
• How do we access the content??
• Extract content from file headers
• Add Keywords
• Content-based Image Retrieval

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Visual Information Systems
Storage
Retrieval
Representation
Indexing
Search Retrieval
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Visual Information
Feature types
Color, texture shape...
Which features should we use? How are we to
organize them? Prioritize? Arrange for Search?
Global Histograms Local Regions Trees...
Examples of search queries
Search for Example like this similar image
features 50 blue and 50 green
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Visual Representation

• Text/Keywords wont do it
• One picture is worth a thousand words
• Standard Object Recognition wont do it
• Our Representation Indexing Goals
• retrieve visual data based on content
• domain independent
• automated

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Image Representation

• Image Processing
• Computer Vision
• Image Representation Pixels to Content

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Image Similarity
Multimedia Object Insertion
Query Multimedia Object
Feature Processing Module
Calculate Similarity
Query Features
Stored Features
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Storage and Retrieval of Images and Video
User Interface
Content-Based Retrieval
Organization
Database Management
Metadata
Database
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Content-based Information Retrieval
Image Pre-Processing
Scene Change Detection
Key-Frame Extraction
Camera Object Motion
Feature Extraction Representation
Camera Motion
Color
Object
Texture
Object Motion
Sketch
Shape
Spatial Relationships
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• Organization Module
• Efficient query processing necessitates
  organization of indices for efficient search
• Image/Video indices
• are approximate
• interrelated multiple attributes
• not ordered
• Need flexible data structures (quad-tree,
  R-tree..)
• Database Management Module
• Physical storage structure and access path to the
  database
• insulation between programs and data
• provides a representation of the data
• supprots multiple views of data
• ensures data consistency

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Video Image GuidedDecision Support System
for Pathology, Univ. of Rutgers
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Evaluation Criteria for Image Retrieval Systems

• Automation
• Multimedia Features
• Adaptability
• Abstraction
• Generality
• Content Collection
• Categorization
• Compressed Domain

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Networked Multimedia for Medical
ImagingRadiology Informatics Lab,Univ. of San
Francisco
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Networked Multimedia for Medical
ImagingRadiology Informatics Lab,Univ. of San
Francisco

• Multimedia Medical Imaging Applications testbed
• Bone age assessment
• Temporal lung node analysis
• Collaborative image consultation
• Noninvasive neurosurgical planning