Morphological Segmentation for Image Processing and Visualization

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Morphological Segmentation for Image Processing
and Visualization

• J.Robarts Research Institute
• London,Canada
• Lixu Gu

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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

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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.

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Morphological Operations -1

• The four most basic operations in mathematical
  morphology are dilation, erosion, opening and
  Closing

Dilation
Erosion
Opening
Closing
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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)

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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 Ri(M,V) is defined as
• Marker M is only allowed to grow in the region
  restricted by mask V.

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Morphological Reconstruction

• Algorithm for binary reconstruction

1. M V o K , where K is any SE. 2. T M, 3.
M M ? Ki , where i4 or i8, 4. M Mn V,
Take only those pixels from M that are also in V
. 5. if M ? T then go to 2, 6. else stop

Opened (M)
Reconstructed (T)
Original (V)
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Application in 2D Image Processing Character
Extraction-1
Character Extraction From Cover Image (Source)
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Application in 2D Image Processing Character
Extraction-2
Character Extraction From Cover Image (Results)
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Application in 2D Image Processing Character
Extraction-3
Morning
Noon
Afternoon
Evening
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Application in 2D Image Processing Character
Extraction-4
Morning
Noon
Afternoon
Evening
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Application in 3D Image Processing Organs
Extraction-1
slice20
slice25
slice30
slice30
slice25
slice20
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Application in 3D Image Processing Organs
Extraction-2
Top View
Back View
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Application in 3D Image Processing Organs
Extraction-3
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Application in 3D Image Processing Organs
Extraction-4
Segmented heart beating cycle
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Application in 3D Image Processing Organs
Extraction-5
Kidney with Vessels
Kidney with Bones
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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

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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.

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Brain View snapshot -1
PGS in a patient brain
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Brain View snapshot -2
Co-registered atlas using PWL
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Brain View snapshot-3 --Registration tool kit

• Features
• Cut plane in 3D
• Work in 2 data sets
• 2D and 3D view
• Registration methods
• LandMark
• ThinPlateSpline
• GridTransform
• MutualInformation

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Mutual Information Registration
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Brain View snapshot-4 --Segmenation tool kit

• Features
• Cut plane in 3D
• Work in 2 data sets
• 2D and 3D view
• Segmenation methods
• Morphology
• Snake
• Level Set
• Watershed

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Research Plan-1

• Medical Image Analysis
• --- Segmentation and Registration
• More efforts address on Ultrasound Image (2D, 3D)

Segmented baby face from US
2D Segmentation using GDM
Real time US, MR integration for IGS
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Research Plan-2

• Image Guided Surgery and Therapy
• Neuro Surgical Navigation
• Patient data acquisition
• Image Visualization
• Surgical Plan
• Surgical Navigation
• Cardiac Surgical Navigation

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Research Plan-3

• Virtual Human
• -- Set up a virtual reality human
• model for surgery plan
• and navigation in the future.
• Virtual Training and Planning

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Research Plan-4

• Robotic Surgery Navigation
• --- Work on human interface

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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.

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Question