Image Management

Dr. Hayit Greenspan Dept of BioMedical Engineering Faculty of Engineering [email protected]

640-7398

Roles for Imaging in Health Care:

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

Image Diagnosis in Dermatology

Fetus Ultrasound

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.

Spinal cord Brain section

MRI Image Diagnosis

Roles for Imaging in Health Care:

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

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).

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 45 0 angles and displaced radially at 20 0 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 Images are at the heart of diagnosis, therapy and follow-up.

• • Digital medical image data in US per year:  10 15 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.

Biomedical Imaging Structural Functional

MRI Ultrasound fMRI X-ray CT Microscopy Projectional x-ray CR Mammograph DSA Modality MRI PET Image Dimension (pixels) 256x256 Gray Level (bits) 12 Avg. Size (Mbytes) 8-20 Emission CT Medical optical imaging SPECT Ultrasound DSA (per run) 512x512 8 512x512or 1024x1024 8 5-10 100-500

Originators

Current Information Systems

Publishers Libraries Users

Originators Repositories Value-added Index Services Users

Digital Libraries

Service X

Multimedia Information Systems:

Work-centered Scenario Maps Legacy Documents Photos Other Collections Databases Co-workers/ Collaborators

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

• 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

• • • • 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 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

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.

Texture Segmentation of MRI images

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

Video: COVIRA Computer Vision in Radiology

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

• 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

We formed image archives How do we access the content??

Extract content from file headers Add Keywords *** Content-based Image Retrieval***

Visual Information Systems

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”

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 Processing • Computer Vision • Image Representation: Pixels to Content

Image Similarity

Multimedia Object Insertion Feature Processing Module Query Multimedia Object Stored Features Calculate Similarity Query Features

Storage and Retrieval of Images and Video

User Interface Content-Based Retrieval Organization Database Management Metadata Database

Content-based Information Retrieval

Scene Change Detection Image Pre-Processing Camera & Object Motion Camera Motion Object Motion Key-Frame Extraction Feature Extraction & Representation Object Color Texture Shape Sketch Spatial Relationships

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

Video: Image Guided Decision Support System for Pathology, Univ. of Rutgers

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 Multimedia application 1 Multimedia application 2 Multimedia application N Medical Image DBMS Data sources Post processing Visualization Communication

Networked Multimedia for Medical Imaging

Radiology Informatics Lab, Univ. of San Francisco Multimedia Medical Imaging Applications testbed: • Bone age assessment • Temporal lung node analysis • Collaborative image consultation • Noninvasive neurosurgical planning