Karunakaran Padmanabhan Satellite and Medical Image Processing
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Transcript of Karunakaran Padmanabhan Satellite and Medical Image Processing
Information Excellence informationexcellence.wordpress.com
Harvesting Information Excellence
Information Excellence2013 Aug Knowledge Share Session
Karunakaran Padmanabhan
“Image Processing” in Satellite and Medical Imaging
Information Excellence informationexcellence.wordpress.com
Karunakaran PadmanabhanSME 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.
Satellite Image Processing & Applications
Automated Image Registration Using Morphological Region of Interest Feature Extraction
Automatic Multiple Source
Integration
Prediction Models
Satellite, Aircraft and Field Data
Improved Data Sets
Validation & Verification
Earth Science Data Integration
Satellite Image Enhancement Indian Remote Sensing Satellite Image
B – Linear stretching Image A-Original
Image enhancement using different Image processing techniques
Image Enhancement
Example: Principal Components
6 spectral imagesfrom an airborneScanner.
Example: Principal Components (cont.)
Component l
1 32102 931.43 118.54 83.885 64.006 13.40
Example: Principal Components (cont.)
Original image After Hotelling transform
Geographical information system
Generation of Thematic map showing different layers of Resources asthemes (layers) for inventory study using satellite image data.
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
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
Image to Image Registration
Incoming Data Image Characteristics(Features) Extraction
• Multi-Temporal Image Correlation
• Landmarking• Coregistration
FeatureMatching
Compute Transform
Image to Map Registration
Input Data
Map
Masking andFeature Extraction
FeatureMatching
Compute Transform
Multi-Sensor Image RegistrationETM/IKONOS Mosaic of Coastal VA Data
IKONOSETM+
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
Image Registration Subsystem Based on a Chip Database
LandmarkChip
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, ExtractWindow 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
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 ofInput 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.
Chip-Window Extraction UsingMathematical 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:
K
Greyscale Morphology: Combined Operationse.g., Erosion + Dilation = Opening
Step 1 (Cont.)
Chip-Window Extraction UsingMathematical 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)
Step 2: Chip-Window RefinedRegistration Using Robust Feature Matching
Reference Chip
Input Window
WaveletDecomposition
WaveletDecomposition
Robust Feature Matching (RFM)
UsingHausdorff Distance
MaximaExtraction
MaximaExtraction
Choice ofBest
Transformation
At EachLevel ofDecomposition{
• Overcomplete Wavelet-type Decomposition: Simoncelli Steerable Pyramid• “Maxima” Extraction: Top 5% of Histogram
Forest Fire detection using Synthetic aperture radar Images
A B
C DDetection 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 classesD – Cross check of burnt areas using optical image (SPOT satellite image)
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).
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-uppurposes, 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 aparticular pathology of for indexing an image database.• Image movement characterization of human organs and articulations.• Data visualization of volumes and dynamic scenes
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Sample Image Processing Applications Image Processing Methods for Medical Imaging
• Image restoration: for removing degradations introduced by theacquisition process (removing bias and amplification correction).• Image segmentation: Thresholding, deformable models, multiresolutionanalysis, mathematical morphology, and many other methods.• Image registration: allowing the comparison of images, being rigid ornon-rigid.• Motion analysis: useful in angiographic sequences or body movementcharacterization.• Morphometrics: shape geometry, similarity among images, ...• Visualization: 2D/3D sementation, multimodality registration andvolume rendering,• Surgery simulation: geometric and biomechanical models of organs andtissues for training.• Medical robotics: robotics surgery
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
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Sample Image Processing Applications Medical Imaging
Enhancing images – Histogram transforms
Histogram Equalization
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Sample Image Processing Applications Medical Imaging
Segmentation of medical images
Original image Initial segmentation Final segmentation
32
Image Fusion: MRI and NMI
MRI (anatomy) NMI (functional)
33
The imaging pipeline
Medical Image Visualization
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 visualinterpretation of medical data
CAS, Srping 2002© L. Joskowicz 36
Medical image visualization
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
3. Segmentation and modeling
• Isolation of relevant anatomical structuresbased on pixel properties
• Model creation for the next computational task– real-time interaction and visualization– simulation– registration, matching, – morphing
Extract clinically useful informationfor a given task or procedure
39
Segmentation and modeling
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
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
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
Virtual and augmented reality
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
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.
Computer aided surgery systems
CAS Clinical applications
• Neurosurgery• Orthopaedics• Maxillofacial, craneofacial, and dental surgery• Laparoscopic and endoscopic surgeries• Radiotherapy• Specific procedures in ophtalmology,
othorhinolaringology, etc.
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Imaging Solution
Research trends & Future Thoughts
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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 retinopathy6. Development of instrumentation – Concentrates on development of scanning laser
ophthalmoscope
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 greatimportance to prevent misunderstanding between human and vice versa.New image recognition system-on-chip (SoC) that realizes a 360-degree automotive topview system on a single chip with driver assistance functions embedded will be anattractive proposition.
• Technical improvement in sensor design and manufacturing are lowering the DAS costand better performance with additional features makes more attractive to new-car buyers
Research in Automotive Imaging
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
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
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Imaging Processing Solution
Questions ?
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Imaging Processing Solution
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