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Image Management. Dr. Hayit Greenspan Dept of BioMedical Engineering Faculty of Engineering hayit@eng.tau.ac.il 640-7398. Roles for Imaging in Health Care:. Diagnosis Assessment and Planning Guidance of Procedures Communication Education and Training Research.

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image management

Image Management

Dr. Hayit Greenspan

Dept of BioMedical Engineering

Faculty of Engineering

hayit@eng.tau.ac.il

640-7398

roles for imaging in health care
Roles for Imaging in Health Care:

Diagnosis

Assessment and Planning

Guidance of Procedures

Communication

Education and Training

Research

slide5
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.

slide6

Spinal cord

Brain section

MRI Image Diagnosis

roles for imaging in health care7
Roles for Imaging in Health Care:

Diagnosis

Assessment and Planning

Guidance of Procedures

Communication

Education and Training

Research

slide8

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).

slide9

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 450angles 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).

medical image databases
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.

slide11

Biomedical Imaging

Structural

Functional

MRI

Ultrasound

fMRI

Medical

optical

imaging

X-ray CT

Microscopy

Projectional

x-ray

Emission

CT

CR

Mammograph

PET

SPECT

DSA

current information systems

Libraries

Users

Current Information Systems

Originators

Publishers

digital libraries

Repositories

Service X

Users

Digital Libraries

Originators

Value-added

Index Services

multimedia information systems work centered scenario

Maps

Legacy Documents

Photos

Other

Collections

Multimedia Information Systems:Work-centered Scenario

Databases

Co-workers/

Collaborators

visual information systems
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

imaging informatics
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

basic concepts in image manipulation
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

contrast enhancement
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.

example of digital subtraction angiography dsa of the bifurcation of the aorta
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.

slide23

VOXEL-MAN(Hamburg): 3D Visualization

http://www.uke.uni-hamburg.de/institute/imdm/idv/index.en.html

Atlasas of brain and other organs: allow views from any viewpoint;

Fusion of modalities +Anatomical atlases

basic concepts in image management
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

evolution of image management in pacs
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.

image management indexing retrieval
Image Management:Indexing & Retrieval

We formed image archives

How do we access the content??

Extract content from file headers

Add Keywords

***Content-based Image Retrieval***

visual information systems28
Visual Information Systems

Storage

Retrieval

Representation

Indexing

Search

&

Retrieval

visual information
Visual Information

Representation

Indexing

Search

&

Retrieval

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”

visual representation
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

image representation
Image Representation

Image Processing

Computer Vision

Image Representation: Pixels to Content

slide32

Image Similarity

Multimedia

Object

Insertion

Query

Multimedia

Object

Feature Processing Module

Calculate Similarity

Query Features

Stored Features

storage and retrieval of images and video
Storage and Retrieval of Images and Video

User Interface

Content-Based

Retrieval

Organization

Database

Management

Metadata

Database

content based information retrieval
Content-based Information Retrieval

Image

Pre-Processing

Scene Change

Detection

Camera &

Object Motion

Key-Frame

Extraction

Feature Extraction & Representation

Camera

Motion

Color

Object

Texture

Object

Motion

Sketch

Shape

Spatial

Relationships

slide35
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

evaluation criteria for image retrieval systems
Evaluation Criteria for Image Retrieval Systems:

Automation

Multimedia Features

Adaptability

Abstraction

Generality

Content Collection

Categorization

Compressed Domain

networked multimedia for medical imaging radiology informatics lab univ of san francisco
Networked Multimedia for Medical ImagingRadiology Informatics Lab,Univ. of San Francisco

Multimedia

application 2

Multimedia

application 1

Multimedia

application N

Medical Image DBMS

Visualization

Data sources

Post-

processing

Communication

networked multimedia for medical imaging radiology informatics lab univ of san francisco42
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