1. Morphological Segmentation for Image Processing and Visualization J.Robarts Research Institute London,Canada Lixu Gu

2. Road Map  Mathematical Morphology  Image Processing: –2D application: Character Extraction –3D application: Medical Image Processing  Image Visualization: BrainView –Registration and Visualization –Segmentation and Visualization  Future Works:

3. Mathematical Morphology  Mathematical morphology is a powerful methodology which was initiated in the late 1960s by G.Matheron and J.Serra at the Fontainebleau School of Mines in France.  nowadays it offers many theoretic and algorithmic tools inspiring the development of research in the fields of signal processing, image processing, machine vision, and pattern recognition.

4. Morphological Operations -1  The four most basic operations in mathematical morphology are dilation, erosion, opening and Closing: DilationErosion OpeningClosing

5. Morphological Operations -2  Top-hat Transformation (TT): –An excellent tool for extracting bright or dark objects –cannot deal with many complicated problems –Difficult to determine proper size of structuring elements automatically  Differential Top-hat Transformation (DTT):

6. Morphological Reconstruction  Conditional Dilation : a special recursive dilation operation (region growing); a powerful function to restore destroyed objective regions. –Let M and V (M  V) be two binary images defined as “marker” and “mask”, respectively. –Conditional dilation R i (M,V) is defined as: –Marker M is only allowed to grow in the region restricted by mask V.

7. Morphological Reconstruction  Algorithm for binary reconstruction: Original (V) Opened (M)Reconstructed (T) 1. M = V o K, where K is any SE. 2. T = M, 3. M= M  K i, where i=4 or i=8, 4. M = M∩ V, [Take only those pixels from M that are also in V.] 5. if M  T then go to 2, 6. else stop; 1. M = V o K, where K is any SE. 2. T = M, 3. M= M  K i, where i=4 or i=8, 4. M = M∩ V, [Take only those pixels from M that are also in V.] 5. if M  T then go to 2, 6. else stop;

8. Application in 2D Image Processing Character Extraction-1 Character Extraction From Cover Image (Source)

9. Application in 2D Image Processing Character Extraction-2 Character Extraction From Cover Image (Results)

10. Application in 2D Image Processing Character Extraction-3 MorningNoon AfternoonEvening

11. Application in 2D Image Processing Character Extraction-4 MorningNoon AfternoonEvening

12. Application in 3D Image Processing Organs Extraction-1 slice20 slice30 slice25 slice30slice25 slice20

13. Application in 3D Image Processing Organs Extraction-2 Top ViewBack View

14. Application in 3D Image Processing Organs Extraction-3

15. Application in 3D Image Processing Organs Extraction-4 Segmented heart beating cycle

16. Application in 3D Image Processing Organs Extraction-5 Kidney with Bones Kidney with Vessels

17. Image Visualization – BrainView  BrainView is a software which I designed and developed at J.Robarts Research Institute, London, Ontario for her industry partner : Cedara Software.  It is designed to visualize the structures of brain and its atlases for stereotaxy surgery navigation (Image Guided Neuro-Surgery).  It is under Python, VTK environment

18. Main Design Issues  Ac-Pc: two anatomic landmarks located in the deep brain used to define the Patient coordinate space  PGS: a Proportional Grid System is designed to segment a brain into 12 sub-regions based on the dimension derived from Ac-Pc Setting.  PWL: a Piece-Wise Linear co-registration technique to warp brain atlases into patient brain space.

19. Brain View snapshot -1 PGS in a patient brain

20. Brain View snapshot -2 Co-registered atlas using PWL

21. Brain View snapshot-3 -- Registration tool kit Features: 1.Cut plane in 3D 2.Work in 2 data sets 3.2D and 3D view 4.Registration methods: LandMark ThinPlateSpline GridTransform MutualInformation

22. Mutual Information Registration

23. Brain View snapshot-4 -- Segmenation tool kit Features: 1.Cut plane in 3D 2.Work in 2 data sets 3.2D and 3D view 4.Segmenation methods: Morphology Snake Level Set Watershed

24. Research Plan-1  Medical Image Analysis --- Segmentation and Registration –More efforts address on Ultrasound Image (2D, 3D) Segmented baby face from US Real time US, MR integration for IGS 2D Segmentation using GDM

25. Research Plan-2  Image Guided Surgery and Therapy: –Neuro Surgical Navigation 1.Patient data acquisition 2.Image Visualization 3.Surgical Plan 4.Surgical Navigation –Cardiac Surgical Navigation

26. Research Plan-3  Virtual Human: -- Set up a virtual reality human model for surgery plan and navigation in the future.  Virtual Training and Planning

27. Research Plan-4  Robotic Surgery Navigation: --- Work on human interface

28. Research Plan-5  Functional MRI (fMRI) for mind study –Research on computer aided acupuncture Find the relationship between acupoint and other organs using fMRI, PET or SPECT technology Visualize acupoint in the human body (eg. Visible Chinese) Find the best procedure for image-guided acupuncture – Other mind study : Vision, Neurosurgical plan, Language, Pain, et.al.

29. Question