Research Topics in Computer Science and Medical Imaging

1. Research Topics in Computer Science and Medical Imaging Gordon Devoe - 2014

2. A Nonlocal Maximum Likelihood Estimation Method for Rician Noise Reduction in MR Images Lili He & Ian Greenshields February 2009

3. Magnetic Resonance (MR) Images • MRI is the newest most versatile medical imaging technique available (very minor fluctuations in chemical composition can be determined). • High resolution images of a patient’s interior body without surgery. • Strong magnets and pulses of radio waves used to manipulate the natural magnetic properties in the body. • Especially useful for capturing images of the brain, spine, organs and soft tissue.

4. Magnetic Resonance (MR) Images

5. Noise in Medical Resonance Images • Sources • Thermal noise from the conductivity of the system’s hardware. • Inductive losses from the conductivity of the object being images. • Factor Trade-offs • Resolution • Signal-to-noise ratio (SNR) • Acquisition speed

6. Denoising MR Images By denoisingmagnetic resonance (MR) images we can improve medical processes including: • Clinical Diagnosis • the determination of the nature of a disease • distinguishing one disease from another. • Tissue Classification • Systematic arrangement of similar tissues on the basis of certain differing characteristics. • Image Segmentation • Coming Soon…

7. Research Topics In Medical Imaging • Image Registration • Image Segmentation

8. Image Registration Methods: A Survey Zitová, Barbara and Flusser, Jan. Image and Vision Computing, ISSN 0262-8856, 2003, Volume 21, Issue 11, pp. 977 - 1000

9. Image Registration • Image registration is one of the most fundamental concepts when using computer science techniques to compare images with one another. • Image registration is the process of overlaying two or more pictures/images of the same scene and comparing them from different viewpoints, times and/or by different sensors. • The intersection of these images involves a set of changes influenced by different imaging conditions or various data sources such as image fusion, change detection and multi-channel image restoration.

10. Image Registration - Applications • Multispectral Classification • Environmental Monitoring • Change Detection • Image Mosaicking • Weather Forecasting • Creating super-resolution images • Geographic information systems (GIS) • Cartography • Computer Vision

11. Image Registration – Applications Medical • Comparing nuclear magnetic resonance spectra data (NRM) with computer tomography (CT) data. • Monitoring tumor growth • Comparing patient data with anatomical atlases (digital libraries of anatomy information). • Brain mapping.

12. Image Registration – 3 Types • Multi-Temporal Image Registration • Images captured at different times/conditions • Evaluate changes in the scene • Tumor evolution monitoring • Healing therapy • Multi-Modal Image Registration • Images from same scene are captures with sensors with different characteristics. • Integrate the information obtained from different source streams to gain more detailed scene representation. • Combine MRI, ultrasound or CT, magnetic resonance spectroscopy for radiotherapy & nuclear medicine applications.

13. Image Registration – 3 Types • Scene-to-model Image Registration • Images of a scene and models are compared. • Models are computer generated and images are overlaid on the computer model. • Comparing a patient’s image with a digital model.

14. Image Registration - Steps • Feature Detection • Salient and distinctive objects (close-boundary regions, edges, contours, edges, lines, corners, etc.) • Features are then represented by their controls points. • Feature Matching • Correspondences between the features detected in the sensed and reference image are established. • Transform Model Estimation • Type and parameters of mapping-functions aligning the sensed/reference image are established. • Image Resampling and transformation • Sensed image is transformed based on the established mapping functions

15. Image Registration - Steps Feature Detection Feature Matching Transform Model Estimation

16. Image Registration – Feature Detection • Features • Salient structures • Regions (forests, lakes, fields) • Lines (region boundaries, coastlines, roads, rivers) • Points (region corners, lines intersections, points on curves) • Stable in time and stay in fixed position • Distinct and spread all over the image. • Features - Medical Domain • Line detection to compare anatomical structures. • Interactive selection. • Introduction of extrinsic features (screw markers, dental adapters, etc.)

17. Image Registration – Feature Matching • Sensed and references images can be compared using • Intensity values • Feature Spatial Distributions • Feature Symbolic Descriptions • Feature Mapping can be divided in to two main categories: • Area-based Feature Detection • Feature Based Feature Detection

18. Image Registration – Area Based • Correlation “Template Matching” • Image intensities are directly matched without any structural analysis. • Sensitive to noise, varying illumination, and/or by using different sensor types. • Useful for real-time applications and easy to implement hardware. figure, imshowpair(peppers(:,:,1),recovered_onion,'blend')

19. Image Registration – Area Based • Mutual Information • Statistically dependency between two data sets. • Comparing anatomical and sensed images of patient’s body. MI criterion (bottom) computed in the neighborhood of point P. Maximum of MI shows correct matching position (point A). Point B indicates the false matching position selected by human operator.

20. Image Registration – Transform Model Est • Once features have been discovered in the sensed/referenced images a mapping function can be constructed to overlay the sensed image onto the reference image. • The mapping function is designed in a way to ensure the correspondence of the control points from the sensed/reference images are as close as possible. • Global mapping models use all control points for estimating one set of mapping function parameters for the entire image. • Local mapping models break the images in to sections making the function parameters depend on the location of their support in the image and the mapping function is defined for each section separately. • Changes in medical images tend to appear locally.

21. Image Registration – Transform Model Est • Similarity Transform (top left) • Affine Transform (top right) • Perspective Projection (bottom left) • Elastic Transform (bottom right)

22. Current Methods in Medical Image Segmentation Dzung L. Pham, ChenyangXu, Jerry L. Prince John Hopkins University Department of Electrical & Computer Engineering

23. Image Segmentation What is image segmentation? • Images are divided up in to smaller pieces or groups of pixels. • Segments are used to simplify or change the original image to a format easier to analyze. • Used to identify objects, lines, curves, etc. within an image. Non-Trivial problem - noise, missing data, overlap of features, etc.

24. Image Segmentation - Medical In the medical domain image segmentation is extremely important when using computers to facilitate the growing number of images in biomedical-imaging applications. • Quantify tissue volumes. • Reconstruction of soft tissues such as the cerebral cortex. • Study of anatomical structures. • Treatment planning • Computer-integrated surgery • Growing number of use cases…

25. Image Segmentation - Methods Image segmentation can be roughly divided in to eight categories: • Thresholding Approaches • Region Growing Approaches • Classifiers • Clustering Approaches • Markov Random Fields Models (extension) • Artificial Neural Networks • Atlas-Guided Approaches

26. Image Segmentation - Thresholding Image segmentation can be achieved by assigning an intensity value to every pixel of an image. Pixels can then be categorized based on whether or not they meet this threshold value.

27. Image Segmentation - Thresholding • Medical Uses • Digital Mammography • Limitations • Only two classes are generated (cancerous/healthy). • Cannot be applied to multichannel images. • Doesn’t take in to account spatial characteristics. • Sensitive to noise and intensity inhomogeneities

28. Image Segmentation – Region Growing A ‘seed point’ is used to extract pixels from a particular part of the image and then transformations are made to the seed to perform further analysis. • Separate regions that have the same properties. • Provide original images with good segmentation results. • Choose multiple criteria at the same time.

29. Image Segmentation – Region Growing Original Figure 155 ~ 255 255 ~ 255 190 ~ 255

30. Image Segmentation - Clustering Image segmentation using clustering algorithms is very similar to segmentation classification methods except clustering algorithms do not require any training data. The K-means algorithm iteratively computes a mean intensity for each class and classifies each pixel of the image to the class with the closest mean.

31. Image Segmentation - Clustering • Advantages • Do not require training data (self-learning). • Lack of spatial modeling provides fast computation. • Disadvantages • Sensitive to noise and intensity inhomogeneities. • Do not directly support spatial modeling. • Potential Algorithms • K-means Algorithm • Fuzzy c-means Algorithm • Expectation-maximization Algorithm

32. Image Segmentation – MRF Models • Statistical model that can be used within segmentation methods such as clustering. • Models spatial interaction between nearby pixels. With MRF K-means Segmentation

33. Image Segmentation – MRF Models Medical images are a good candidate for Markov Random Field (MRF) models because pixels are generally in the same area as others from the same class and are widely used in digital mammograms. • A depends on B and D • B depends on D and A • C Depends on E • Etc.

34. Image Segmentation – Deformable Models • Deformable models delineate region boundaries by using closed parametric curves or surfaces. • These curves and surfaces deform when under the influence of internal or external forces

35. Image Segmentation – Deformable Models Advantages • Ability to directly generate closed parametric curves or surfaces from images. • Incorporation of a smoothness constraint that provides robustness to noise and spurious edges. Disadvantages • Manual interaction is required to place initial model and choose appropriate parameters. • Deformable models exhibit poor convergence to concave boundaries.

36. Image Segmentation – Deformable Models Deformable surface used for the reconstruction of the cerebral cortex. Intersection between deformable surface and orthogonal slices of the MR image.

37. Image Segmentation – Atlas Guided • When an atlas or template is available atlas-guided image segmentation methods can be employed to anatomically separate features. • A template can simply be a collection of information about the anatomy of an image being segmented. • A Template can continually be re-used to segment other images of the same type. • By using an original image and the pre-segmented template a ‘warped’ image can be generated by using linear and non-linear transformations.

38. Image Segmentation – Atlas Guided • Template Image • Target Image • Warped Template

39. Image Segmentation – Atlas Guided Three slices from a MR brain volume image overlaid with the atlas.

40. Image Segmentation – Future Work • Future work • Improving accuracy. • Incorporating prior information from atlases. • Increasing precision tolerances. • Combine discrete & continuous spatial-domain segmentation methods. • Improve computational speed of segmentation methods. • Multiscale processing • Parallelizable methods • Reduce amount of manual interaction.

41. Questions

42. References Medical Image Registration Image registration methods: a survey by Zitová, Barbara and Flusser, Jan. Image and Vision Computing, ISSN 0262-8856, 2003, Volume 21, Issue 11, pp. 977 – 1000 Q. Chen, M. Defrise, F. Deconinck, Symmetric phase-only matched filtering of Fourier–Mellin transform for image registration and recognition, IEEE Transactions on Pattern Analysis and Machine Intellingence 16 (1994) 1156–1168. Multimodality image registration by maximization of mutual information by Maes, F; Collignon, A; Vandermeulen, D; Marchal, G; Suetens, P. IEEE transactions on medical imaging, ISSN 0278-0062, 04/1997, Volume 16, Issue 2, pp. 187 – 198 Medical Image Segmentation Current methods in medical image segmentation by Pham, D L; Xu, C; Prince, J L Annual review of biomedical engineering, ISSN 1523-9829, 2000, Volume 2, p. 315 http://en.wikipedia.org/wiki/Image_segmentation http://en.wikipedia.org/wiki/Region_growing http://en.wikipedia.org/wiki/Markov_random_field Magnetic Resonance Imaging (MRI) Noise Reduction A nonlocal maximum likelihood estimation method for Rician noise reduction in MR images by He, Lili and Greenshields, Ian R IEEE transactions on medical imaging, ISSN 0278-0062, 02/2009, Volume 28, Issue 2, pp. 165 - 172