Karunakaran Padmanabhan Satellite and Medical Image Processing


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Karunakaran Padmanabhan Satellite and Medical Image Processing

  1. 1. Information Excellence informationexcellence.wordpress.com Harvesting Information Excellence Information Excellence 2013 Aug Knowledge Share Session Karunakaran Padmanabhan “Image Processing” in Satellite and Medical Imaging
  2. 2. Information Excellence informationexcellence.wordpress.com Karunakaran Padmanabhan SME and Industry Expert, Image Processing Today’s Topic Topic Abstract: Application of Image processing as a product is emerging in areas such as Automotive Driver assistance system, Security & surveillance, Machine vision, Mobile Image applications, medical imaging diagnosis and other area of imaging research as accelerator is in progress on many areas. Improvement in sensor technology, reducing sensor cost and increase in the processing power has dramatically improved and increased the imaging applications. A vivid description of image processing applications and forethought on future research is also discussed.
  3. 3. kpadma31@rediffmail.com IMAGE PROCESSING in Satellite and Medical Applications KARUNAKARAN PADMANABHAN
  4. 4. Satellite Image Processing & Applications
  5. 5. Automated Image Registration Using Morphological Region of Interest Feature Extraction
  6. 6. Automatic Multiple Source Integration Prediction Models Satellite, Aircraft and Field Data Improved Data Sets Validation & Verification Earth Science Data Integration
  7. 7. Satellite Image Enhancement Indian Remote Sensing Satellite Image B – Linear stretching ImageA-Original Image enhancement using different Image processing techniques
  8. 8. Image Enhancement
  9. 9. Example: Principal Components 6 spectral images from an airborne Scanner.
  10. 10. Example: Principal Components (cont.) Component l 1 3210 2 931.4 3 118.5 4 83.88 5 64.00 6 13.40
  11. 11. Example: Principal Components (cont.) Original image After Hotelling transform
  12. 12. Geographical information system Generation of Thematic map showing different layers of Resources as themes (layers) for inventory study using satellite image data.
  13. 13. Image Registration • Navigation or Model-Based Systematic Correction – Orbital, Attitude, Platform/Sensor Geometric Relationship, Sensor Characteristics, Earth Model, ... • Image Registration or Feature-Based Precision Correction – Navigation within a Few Pixels Accuracy – Image Registration Using Selected Features (or Control Points) to Refine Geo-Location Accuracy • 2 Approaches: (1) Image Registration as a Post-Processing (2) Navigation and Image Registration in a Closed Loop
  14. 14. Image Registration Challenges • Multi-Resolution / Mono- or Multi-Instrument • Multi-temporal data • Various spatial resolutions • Various spectral resolutions • Sub-Pixel Accuracy • 1 pixel misregistration=> 50% error in NDVI computation • Accuracy Assessment • Synthetic data • "Ground Truth" (manual registration?) • Use down-sampled high-resolution data • Consistency ("circular" registrations) studies
  15. 15. Image to Image Registration Incoming Data Image Characteristics (Features) Extraction • Multi-Temporal Image Correlation • Landmarking • Coregistration Feature Matching Compute Transform
  16. 16. Image to Map Registration Input Data Map Masking and Feature Extraction Feature Matching Compute Transform
  17. 17. Multi-Sensor Image Registration ETM/IKONOS Mosaic of Coastal VA Data IKONOS ETM+
  18. 18. Image Registration Components 0 Pre-Processing • Cloud Detection, Region of Interest Masking, ... 1 Feature Extraction (“Control Points”) • Edges, Regions, Contours, Wavelet Coefficients, ... 2 Feature Matching • Spatial Transformation (a-priori knowledge) • Search Strategy (Global vs Local, Multi-Resolution, ...) • Choice of Similarity Metrics (Correlation, Optimization Method, Hausdorff Distance, ...) 3 Resampling, Indexing or Fusion
  19. 19. Image Registration Subsystem Based on a Chip Database Landmark Chip Database UTM of 4 Scene Corners Known from Systematic Correction Input Scene (1) Find Chips that Correspond to the Incoming Scene (2) For Each Chip, Extract Window from Scene, Using UTM of: - 4 Approx Scene Corners - 4 Correct Chip Corners (3) Register Each (Chip,Window) Pair and Record Pairs of Registered Chip Corners (4) Compute Global Registration from Multiple Local Ones (5) Compute Correct UTM of 4 Scene Corners of Input Scene
  20. 20. Image Registration Subsystem Based on Automatic Chip Extraction UTM of 4 Scene Corners Known from Systematic Correction Input Scene (1) Extract Reference Chips and Corresponding Input Windows Using Mathematical Morphology (2) Register Each (Chip,Window) Pair and Record Pairs of Registered Chip Corners (refinement step) (3) Compute Global Registration from Multiple Local Ones (4) Compute Correct UTM of 4 Scene Corners of Input Scene Reference Scene Advantages: • Eliminates Need for Chip Database • Cloud Detection Can Easily be Included in Process • Process Any Size Images • Initial Registration Closer to Final Registration => Reduces Computation Time and Increases Accuracy.
  21. 21. Chip-Window Extraction Using Mathematical Morphology Mathematical Morphology (MM) Concept: • Nonlinear spatial-based technique that provides a framework. • Relies on a partial ordering relation between image pixels. • In greyscale imagery, such relation is given by the digital value of image pixels Structuring element Dilation 3x3 structuring element defines neighborhood around pixel P Erosion Max Min P Original image Dilation 3x3 structuring element defines neighborhood around pixel P Erosion Max Min P Original image Original image Erosion K K Dilation (4-pixel radius Disk SE) Greyscale MM Basic Operations:
  22. 22. K Greyscale Morphology: Combined Operations e.g., Erosion + Dilation = Opening Step 1 (Cont.)
  23. 23. Chip-Window Extraction Using Mathematical Morphology Results(Landsat-7/ETM+ Data - Central VA) 10 Chips Extracted from Reference Scene (Oct. 7, 1999) 10 Windows Extracted from Input Scene (Nov. 8, 1999)
  24. 24. Step 2: Chip-Window Refined Registration Using Robust Feature Matching Reference Chip Input Window Wavelet Decomposition Wavelet Decomposition Robust Feature Matching (RFM) Using Hausdorff Distance Maxima Extraction Maxima Extraction Choice of Best Transformation At Each Level of Decomposition{ • Overcomplete Wavelet-type Decomposition: Simoncelli Steerable Pyramid • “Maxima” Extraction: Top 5% of Histogram
  25. 25. Forest Fire detection using Synthetic aperture radar Images A B C D Detection of Forest Fire using ERS-SAR Images A –Original 1976 FCC image (Before Forest Fire) B- Image of Burnt areas (After Forest Fire) C- Classification of Burnt area to different classes D – Cross check of burnt areas using optical image (SPOT satellite image)
  26. 26. Medical Imaging • Medical imaging is the technique and process used to create images of the human body (or parts and function thereof) for clinical purposes (medical procedures seeking to reveal, diagnose, or examine disease) or medical science (including the study of normal anatomy and physiology).
  27. 27. enVision Progress Sample Image Processing Applications Medical Imaging Medical images give information of shape and function of organs of human body, being one of the most important mean for establishing the diagnosis. • Medical images are a special mean for controlling the therapeutic action. A medical doctor uses images for diagnosis, together with many other information. In most of the cases it is qualitative and subjective evaluation. • The information conveyed by medical images is very difficult to exploit quantitatively and objectively. The new capabilities offered by Image processing for diagnosis • Quantitative measurement of several image parameters (colour, shape, texture) in 2D or 3D. • Change detection among images acquired in different instants. The time interval can be a few seconds as in an angiographic sequence or several months, for follow-up purposes, using images of the same modality. • Data fusion, among different imaging modalities, allowing the combination of complementary information of the same patient Comparison of images from the same imaging modality, but from different patients. This will be useful for studying a particular pathology of for indexing an image database. • Image movement characterization of human organs and articulations. • Data visualization of volumes and dynamic scenes
  28. 28. enVision Progress Sample Image Processing Applications Image Processing Methods for Medical Imaging • Image restoration: for removing degradations introduced by the acquisition process (removing bias and amplification correction). • Image segmentation: Thresholding, deformable models, multiresolution analysis, mathematical morphology, and many other methods. • Image registration: allowing the comparison of images, being rigid or non-rigid. • Motion analysis: useful in angiographic sequences or body movement characterization. • Morphometrics: shape geometry, similarity among images, ... • Visualization: 2D/3D sementation, multimodality registration and volume rendering, • Surgery simulation: geometric and biomechanical models of organs and tissues for training. • Medical robotics: robotics surgery
  29. 29. Image Processing Operations  Blur  Image Rotation and Scaling  Noise Removal  Histogram Operations  Median Filter  Gaussian Filter  STC Filter  Luminance Filter  Gamma Correction  High and Low pass Filters  Morphological operations Image Analysis Techniques  Skew / DeSkew  Segmentation  Edge detection  Blob Detection  Region Growing  Image Stitching  Pattern Feature Extraction  Measurements Diagnostic Tools
  30. 30. enVision Progress Sample Image Processing Applications Medical Imaging Enhancing images – Histogram transforms Histogram Equalization
  31. 31. enVision Progress Sample Image Processing Applications Medical Imaging Segmentation of medical images Original image Initial segmentation Final segmentation
  32. 32. 32 Image Fusion: MRI and NMI MRI (anatomy) NMI (functional)
  33. 33. 33 The imaging pipeline
  34. 34. Medical Image Visualization
  35. 35. 35 2. Medical image visualization • 3D visualization of complex structures • image correlation and fusion • quantitative measurements and comparisons • visualization of medical and CAD data Enhance diagnosis by improving the visual interpretation of medical data
  36. 36. CAS, Srping 2002 © L. Joskowicz 36 Medical image visualization
  37. 37. 37 Medical image visualization • Much activity! Radiologists are the experts • Commercial packages – 3DVIEWNIX, ANALYZE, IMIPS,ITK,MVITK • Main topics: – 3D volume rendering techniques – 3D image filtering and enhancement – surface construction algorithms: Marching cubes, etc.
  38. 38. 38 3. Segmentation and modeling • Isolation of relevant anatomical structures based on pixel properties • Model creation for the next computational task – real-time interaction and visualization – simulation – registration, matching, – morphing Extract clinically useful information for a given task or procedure
  39. 39. 39 Segmentation and modeling
  40. 40. 40 Segmentation and modeling: technical needs • Segmentation: – landmark feature detection – isosurface construction (Marching cubes) – contour extraction, region identification • Modeling: – points, anatomical landmarks, surface ridges – surfaces as polygon meshes, surface splines – model simplification methods (Alligator, Wrapper)
  41. 41. 41 Segmentation and modeling • Medical images have very special needs! • Commercial packages – 3DVIEWNIX, ANALYZE, IMIPS • Main topics: – Volumetric segmentation techniques for CT, MRI – 2D and 3D segmentation with deformable elements – surface and model simplification algorithms
  42. 42. 42 4. Virtual and augmented reality • Create a virtual model for viewing during surgery • Project the model on the patient or integrate with surgeon’s view • Useful for intraoperative anatomy exploration and manipulation • Telesurgery systems Use images to create or enhance a surgical situation
  43. 43. 43 Virtual and augmented reality
  44. 44. 44 Visualization: Technical needs • image enhancing and noise reduction • image interpolation: images from new viewpoints • 3D visualization from 2.5D data – volume rendering: display voxels and opacity values – surface rendering: explicit reconstruction of surface • 3D modeling from 2.5D data • 2D and 3D segmentation • 3D+T visualization (beating heart)
  45. 45. 45 Medical image visualization • Much activity! Radiologists are the experts • Commercial packages – 3DVIEWNIX, ANALYZE, IMIPS • Main topics: – 3D volume rendering techniques – 3D image filtering and enhancement – surface construction algorithms: Marching cubes, etc.
  46. 46. Computer aided surgery systems
  47. 47. CAS Clinical applications • Neurosurgery • Orthopaedics • Maxillofacial, craneofacial, and dental surgery • Laparoscopic and endoscopic surgeries • Radiotherapy • Specific procedures in ophtalmology, othorhinolaringology, etc.
  48. 48. enVision Progress Imaging Solution Research trends & Future Thoughts
  49. 49. enVision Progress Current research in Medical Imaging A wide research is being done in the Image processing technique. 1. Cancer Imaging – Different tools such as PET, MRI, and Computer aided Detection helps to diagnose and be aware of the tumor. 2. Brain Imaging – Focuses on the normal and abnormal development of brain, brain ageing and common disease states. 3. Image processing – This research incorporates structural and functional MRI in neurology, analysis of bone shape and structure, development of functional imaging tools in oncology, and PET image processing software development. 4. Imaging Technology – Development in image technology have formed the requirement to establish whether new technologies are effective and cost beneficial. This technology works under the following areas: · Magnetic resonance imaging of the knee · Computer aided detection in mammography · Endoscopic ultrasound in staging the esophageal cancer · Magnetic resonance imaging in low back pain · Ophthalmic Imaging – This works under two categories: 5. Development of automated software- Analyzes the retinal images to show early sign of diabetic retinopathy 6. Development of instrumentation – Concentrates on development of scanning laser ophthalmoscope
  50. 50. Questions? • Traffic sign recognition technology as a promising function in the automobile industry and expect it to grow in many regions including Europe. Map data and positioning information of navigation systems is being developed to improve driver assistance functions. The next step for driver assistance, i.e. linking vehicles together wirelessly through sensor technology. • Vehicle-to-vehicle and vehicle-to-infrastructure based driver assistance systems are perhaps some of the DAS technologies, and human machine interface is of great importance to prevent misunderstanding between human and vice versa. New image recognition system-on-chip (SoC) that realizes a 360-degree automotive top view system on a single chip with driver assistance functions embedded will be an attractive proposition. • Technical improvement in sensor design and manufacturing are lowering the DAS cost and better performance with additional features makes more attractive to new-car buyers Research in Automotive Imaging
  51. 51. Questions? Image recognition to multi-sensor applications - concrete applications in advertising, image analysis, and other indoor and outdoor applications. The MOBVIS system can recognize individual buildings in a photo you take with your camera-phone. Then it can apply icons that hyperlink to information about the building. Simply by looking at a picture, the system knows where you are and can tell what you are looking at digital mapping and navigation solutions. MOBVIS technology to detect roads, people, cars, signs, text, and other details from video sequences acquired from the mobile mapping Multi-sensor information, such as from GPS and inertial sensors, are available in current mobile phone technology and ready to be exploited for innovative services. Imagine simply by wearing a wristband, you could recognize the wearer’s activities, such as sitting, standing, walking, cycling, or running in real-time . The mobile phone will just become our personal multi-sensor magic wand to discover unknown stories in intuitive interaction with our environment. Automatic face recognition can quickly attach a name to a face by searching a large database of face images and finding the closest match. This is what law enforcement agencies typically do for mug shot databases .Law enforcement agencies are using facial recognition software as a crime-fighting tool. Now businesses are looking to use the technology to reach customers  Facial recognition software in its digital signage displays, The displays use touch screens to interact with the customer and feature everything from video and graphics to Internet sites and broadcast clips. The technology also identifies general characteristics like gender, age and race and tracks how customers use the display and for how long. system promises anonymity as it builds a digital customer profile that includes physical characteristics calls a "marketing avatar" or "mavatar. New Research Trends in Image processing
  52. 52. The recent launch of Google Glass is already sparking a debate over privacy and could create a slippery slope, The person wearing the glasses can discreetly snap a photo with a simple wink of their eye. He offers the following example as a reason to exercise caution. "If someone can use Google Glass or his cell phone to take a picture of you and use it to search for you in Facebook or on Google, then he could dig out all kinds of personal information about you while you are shopping or driving down the street .Handwriting-Based Tool Offers Alternate Lie Detection Method Computer Chip Based On Human Brain Developed -Today's computing chips are incredibly complex and contain billions of nano-scale transistors, allowing for fast, high-performance computers, pocket-sized smart phones that far outpace early desktop computers, and an explosion in handheld tablets.  Recognizing People by the Way They Walk -Recognizing people by the way they walk can have numerous applications in the fields of security, leisure or medicine. development of this new biometric technique that takes into account the way a person walks and his/her silhouette. The technique offers significant advantages as recognition can be done remotely and does not require the cooperation of the subject  Eye-Tracking Could Outshine Passwords If Made User-FriendlyFacebook Use Predicts Declines in Happiness,  Software to Detect Forged Photos  Brand protection , Copyright protection – detection and tracking of the use of copyright images online and in print Automate and speed up the process of identifying visual content online and in the press. Visual Content Tracking Automate the tracking of copyright images, advertisements, logos and product images with image recognition to improve efficiency and reduce human error  Augmented Reality - New Research Trends in Image processing
  53. 53. enVision Progress Imaging Processing Solution Questions ?
  54. 54. enVision Progress Imaging Processing Solution Thanks
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