1 / 18

AUTOMATIZATION OF COMPUTED TOMOGRAPHY PATHOLOGY DETECTION

AUTOMATIZATION OF COMPUTED TOMOGRAPHY PATHOLOGY DETECTION. Semyon Medvedik Elena Kozakevich. Computed Tomography. Advanced Diagnostic Tool The scan is performed through taking large amount of 2D X-Rays images. There are two possible way of perform the scan: “Slices” scan “Spiral” scan

judd
Download Presentation

AUTOMATIZATION OF COMPUTED TOMOGRAPHY PATHOLOGY DETECTION

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. AUTOMATIZATION OF COMPUTED TOMOGRAPHY PATHOLOGYDETECTION Semyon Medvedik Elena Kozakevich

  2. Computed Tomography • Advanced Diagnostic Tool • The scan is performed through taking large amount of 2D X-Rays images. • There are two possible way of perform the scan: • “Slices” scan • “Spiral” scan • The images then combined into pseudo 3D image

  3. The Analysis: Manual • After CT scan is done, the result images are transferred to expert radiologist. • The qualified radiologist examines the scans. • All visible pathology found • Then analysis is transferred to patient’s physician.

  4. Analysis: Automatic – Why? • Analysis is done by human – slow! • Analysis is done by human – expensive! • Human DO make mistakes! • Hence, the analysis is not always detailed

  5. Analysis - Automatic Can we develop the software, that not only perform diagnostic scan, but also will be able to determine the diagnostic by itself? Let's try!

  6. What we decided to do with that? Develop a method that generalizes all possible CT scans!

  7. HOW? • Create averaged image of healthy CT scans • Database • Average • Median • Compare input image to the database image • Edge detection • Distance transform • Additional Techniques • Segmentation

  8. Results

  9. Brain CT • Template Matching • Region Oriented Segmentation • Edge Oriented Segmentation

  10. Analysis: Automatic • Each part of the human anatomy must be observed from different point of view • Lungs CT • Abdominal CT • Liver CT

  11. Lungs CT • Gray leveled filters • Segmentation precipitation • Edge detection • Rolling Ball Algorithm

  12. Abdominal CT • Active – Contour Approach • Center – discover to Region of Interest • Size – Use of previous knowledge • Average Image Intensity - Each pixel inside found contour effects the results • Aspect Ratio - Additional tools to refine the results.

  13. Liver CT • Image simplification as preprocessing • Detecting a search range – morphological filter • Contour based segmentation – labeling based search algorithm

  14. Discussion • Each of the described methods uses different approaches • Every of the mentioned methods use segmentation as preprocessing. • In our experiments segmentation returned the best result. • Conclusion: Segmentation is essential for automatic interpretation of the scans.

  15. Why is this issue so difficult? • Each of the organs mentioned above has absolutely different structure, hence in the CT scan image it has different shape, intensity and texture. • The adjacent organs may have similar attributes as the examined one, therefore the analysis may become very difficult even by radiologist.

  16. Conclusions Medical image analysis continues to be an active area of research. It has many difficult challenges ahead, both in terms of addressing the practical need of community, as well as the theoretical side of CAD.

  17. Questions?

More Related