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Institute of
                              Information Systems




Region-based volumetric medical image
              retrieval




                Antonio Foncubierta Rodríguez
                               Henning Müller
                          Adrien Depeursinge
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
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
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
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
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
Methods overview   Institute of
                   Information Systems
Methods              Institute of
                     Information Systems

1. Resampling
  •   Cubic voxels
  •   1mm side

For each scale s:
2. Difference of
   Gaussians is
   computed
Methods                          Institute of
                                 Information Systems

3. Find regional minima
  •   Fill hole algorithm on
      the DoG image
  •   Substract the DoG
      image to the hole filled
  •   Result: Map or regional
      minima
4. Find regional maxima
  •   Grind-peak algorithm
Methods                          Institute of
                                 Information Systems

5. Logical OR on maxima
   and minima
6. Opening
  •   Ball structuring element
  •   Radius proportional (r)
      to scale
7. Label connected
   components
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
Examples   Institute of
           Information Systems
Examples   Institute of
           Information Systems
Comparison with segmentation   Institute of
                               Information Systems
Integration into a retrieval                Institute of
                                            Information Systems
application
• Descriptors integrated in the detector:
  • Basic descriptors: statistical moments of gray level
    values
  • Wavelet descriptors: energy of the wavelet
    coefficients in each region
Integration into a retrieval   Institute of
                               Information Systems
application
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
Institute of
                                                                  Information Systems




 Thanks for your attention!

                     More information at http://medgift.hevs.ch


Antonio 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

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Region-based volumetric medical image retrieval

  • 1. Institute of Information Systems Region-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 Systems 1. Resampling • Cubic voxels • 1mm side For each scale s: 2. Difference of Gaussians is computed
  • 9. Methods Institute of Information Systems 3. Find regional minima • Fill hole algorithm on the DoG image • Substract the DoG image to the hole filled • Result: Map or regional minima 4. Find regional maxima • Grind-peak algorithm
  • 10. Methods Institute of Information Systems 5. Logical OR on maxima and minima 6. Opening • Ball structuring element • Radius proportional (r) to scale 7. 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 Systems application • 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 Systems application
  • 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.ch Antonio 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