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Region-based volumetric medical image retrieval
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Region-based volumetric medical image retrieval

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Volumetric medical images contain an enormous amount of visual information that can discourage the exhaustive use of local descriptors for image analysis, comparison and retrieval. Distinctive......

Volumetric medical images contain an enormous amount of visual information that can discourage the exhaustive use of local descriptors for image analysis, comparison and retrieval. Distinctive features and patterns that need to be analyzed for nding diseases are most often local or regional, often in only very small parts of the image. Separating the large amount of image data that might contain little important information is an important task as it could reduce the current information overload of physicians and make clinical work more ecient. In this paper a novel method for detecting key-regions is introduced as a way of extending the concept of keypoints often used in 2D image analysis. In this way also computation is reduced as important visual features are only
extracted from the detected key regions.

The region detection method is integrated into a platform{independent, web-based graphical interface for medical image visualization and retrieval in three dimensions. This web- based interface makes it easy to deploy on existing infrastructures in both small and large-scale clinical environments.

By including the region detection method into the interface, manual annotation is reduced and time is saved, making it possible to integrate the presented interface and methods into clinical routine and work ows, analyzing image data at a large scale.

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  • 1. Institute of Information SystemsRegion-based volumetric medical image retrieval Antonio Foncubierta Rodríguez Henning Müller Adrien Depeursinge
  • 2. The need for retrieval Institute of Information Systems• Millions of medical images are produced everyday worldwide • Quickly increasing• 30% of world storage capacity• Retrieval methods can improve reuse for: • Training • Decision support
  • 3. Describing medical images Institute of Information Systems• Images contain large amounts of information • CT Scan: 512x512x200 = ~50 Million voxels• In medical images, features occur in small zones: • Irrelevant information: discarded • Relevant information: locally described
  • 4. Local description Institute of Information Systems• Common local image analysis options: • Dense sampling • Salient key points (2D): • SIFT • Superpixels• No preferred method in exists in 3D for now • But 3D data needs local analysis even more
  • 5. Current Challenges Institute of Information Systems• Point-based techniques: • How are points chosen? • How many points are enough? • How to integrate information from neighborhoods?• Segmentation-based techniques: • Application-specific: not reusable for other image types or anatomical parts • Local descriptors of large regions become global descriptors
  • 6. Multiscale Salient Region Detector Institute of Information Systems• Saliency-based: • Detects where features will be useful • No a priori decision of how many regions • Reusable in all images where saliency occurs• Region-based: • Relevant neighborhood is immediately provided• Multiscale: • Large and small complementary regions are detected
  • 7. Methods overview Institute of Information Systems
  • 8. Methods Institute of Information Systems1. Resampling • Cubic voxels • 1mm sideFor each scale s:2. Difference of Gaussians is computed
  • 9. Methods Institute of Information Systems3. Find regional minima • Fill hole algorithm on the DoG image • Substract the DoG image to the hole filled • Result: Map or regional minima4. Find regional maxima • Grind-peak algorithm
  • 10. Methods Institute of Information Systems5. Logical OR on maxima and minima6. Opening • Ball structuring element • Radius proportional (r) to scale7. Label connected components
  • 11. Parameters Institute of Information Systems• Scale progression s By default s ranges from 2 to 16 in powers of 2• Thresholding parameter k Controls the minimum saliency Larger k values produce fewer regions• Radius parameter r Controls the minimum size of detected regions Larger r values produce smooth, large regions only, removing small ones
  • 12. Examples Institute of Information Systems
  • 13. Examples Institute of Information Systems
  • 14. Comparison with segmentation Institute of Information Systems
  • 15. Integration into a retrieval Institute of Information Systemsapplication• Descriptors integrated in the detector: • Basic descriptors: statistical moments of gray level values • Wavelet descriptors: energy of the wavelet coefficients in each region
  • 16. Integration into a retrieval Institute of Information Systemsapplication
  • 17. Conclusions Institute of Information Systems• Medical image retrieval requires local analysis• A region-of-interest detector coupled with a descriptor can enable retrieval: • Multi-scale regions • No predefined number of regions • No predefined shape• Good results compared to manual segmentation of ROIs• Integration into web-based retrieval system for better adoption in clinical practice
  • 18. Institute of Information Systems Thanks for your attention! More information at http://medgift.hevs.chAntonio Foncubierta-Rodríguez, Henning Müller and Adrien Depeursinge, Region-based volumetric medical image retrieval, in: SPIE Medical Imaging: Advanced PACS-based Imaging Informatics and Therapeutic Applications, Orlando, FL, USA, SPIE, 2013