1 / 42

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

adah
Download Presentation

Image Management

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Image Management Dr. Hayit Greenspan Dept of BioMedical Engineering Faculty of Engineering hayit@eng.tau.ac.il 640-7398

  2. Roles for Imaging in Health Care: Diagnosis Assessment and Planning Guidance of Procedures Communication Education and Training Research

  3. Image Diagnosis in Dermatology

  4. Fetus Ultrasound

  5. 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.

  6. Spinal cord Brain section MRI Image Diagnosis

  7. Roles for Imaging in Health Care: Diagnosis Assessment and Planning Guidance of Procedures Communication Education and Training Research

  8. 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).

  9. 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).

  10. 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.

  11. Biomedical Imaging Structural Functional MRI Ultrasound fMRI Medical optical imaging X-ray CT Microscopy Projectional x-ray Emission CT CR Mammograph PET SPECT DSA

  12. Libraries Users Current Information Systems Originators Publishers

  13. Repositories Service X Users Digital Libraries Originators Value-added Index Services

  14. Maps Legacy Documents Photos Other Collections Multimedia Information Systems:Work-centered Scenario Databases Co-workers/ Collaborators

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. Texture Segmentation of MRI images

  21. 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

  22. Video: COVIRAComputer Vision in Radiology

  23. 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

  24. 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.

  25. 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***

  26. Visual Information Systems Storage Retrieval Representation Indexing Search & Retrieval

  27. 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”

  28. 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

  29. Image Representation Image Processing Computer Vision Image Representation: Pixels to Content

  30. Image Similarity Multimedia Object Insertion Query Multimedia Object Feature Processing Module Calculate Similarity Query Features Stored Features

  31. Storage and Retrieval of Images and Video User Interface Content-Based Retrieval Organization Database Management Metadata Database

  32. 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

  33. 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

  34. Video: Image GuidedDecision Support System for Pathology, Univ. of Rutgers

  35. Evaluation Criteria for Image Retrieval Systems: Automation Multimedia Features Adaptability Abstraction Generality Content Collection Categorization Compressed Domain

  36. 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

  37. 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

More Related