karunakaran padmanabhan satellite and medical image processing

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Information Excellence2013 Aug Knowledge Share Session

Karunakaran Padmanabhan

“Image Processing” in Satellite and Medical Imaging


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.

[email protected]

IMAGE PROCESSINGin Satellite and Medical Applications

KARUNAKARAN PADMANABHAN

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).

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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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Enhancing images – Histogram transforms

Histogram Equalization

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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.

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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

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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)

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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

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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

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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)

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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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