Medical Imaging Projects

1. Medical Imaging Projects Daniela S. Raicu, PhD Assistant Professor Email: draicu@cs.depaul.edu Lab URL: http://facweb.cs.depaul.edu/research/vc/

2. IMP & MediX Labs @ DePaul Faculty: GM. Besana, L. Dettori, J. Furst, G. Gordon, S. Jost, D. Raicu, N. Tomuro CTI Students: W. Horsthemke, C. Philips, R. Susomboon, J. Zhang E. Varutbangkul, S.G. Valencia • IMP Collaborators & Funding Agencies • National Science Foundation (NSF) - Research Experience for Undergraduates (REU) • Northwestern University - Department of Radiology, Imaging Informatics Section • University of Chicago – Medical Physics Department • Argonne National Laboratory - Biochip Technology Center • MacArthur Foundation

3. Outline • Medical Imaging and Computed Tomography • Soft Tissue Segmentation in Computed Tomography • Project 1: Region-based classification • Project 2: Texture-based snake approach • Content-based Image Retrieval and Annotation • Project 3: Lung Nodule Retrieval based on image content and radiologists’ feedback • Project 4: Associations discovery between image content and radiologists’ assessment

4. X-Ray CT fMRI What is Medical Imaging (MI)? The study of medical imagingis concerned with the interaction of all forms of radiation with tissue and the development of appropriate technology to extract clinically useful information from observation of this technology.

5. Computed Tomography (CT) • G. Hounsfield (computer expert) and A.M. Cormack (physicist) (Nobel Prize in Medicine in 1979) • CT overcomes limitations of plain radiography • CT doesn’t superimpose structures (like X-ray) • CT is an imaging based on a mathematical formalism that states that if an object is viewed from a number of different angles than a cross-sectional image of it can be computed (reconstructed) _______________________________________________

6. CT Data • Stages of construction of a voxel dataset from CT data • CT data capture works by taking many one dimensional • projections through a slice (scanning) • (b) CT reconstruction pipeline

7. CT – Data Acquisition Slice-by-slice acquisition • X-ray tube is rotating around patient to acquire a slice • patient is moved to acquire the next slice Volume acquisition • X-ray tube is moving continuously along a spiral (helical) path and the data is acquired continuously _______________________________________________

8. CT – Data Acquisition • slice-by-slice scanning • (b) Spiral (volume) scanning

9. CT – SPIRAL SCANNING • a patient is moved 10mm/s (24cm / single scan) • slice thickness: 1mm-1cm • faster than slice-by-slice CT • no shifting of anatomical structures • slice can be reconstructed with an arbitrary orientation with (a single breath) volume • CT multi-slice systems: • • parallel system of detectors • 4/8/16 slices at a time • • generates a large data of thin slices • • better spatial resolution ( better reconstruction)

10. CT - DATA PROCESSING CT numbers (Hounsfield units) HU: • computed via reconstruction algorithm (~tissue density/ X-ray absorption) • most attenuation (bone) • least attenuation (air) • blood/calcium increases tissue density Understanding Visual Information: Technical, Cognitive and Social Factors

11. CT - DATA PROCESSING Relationship between CT numbers and brightness level Understanding Visual Information: Technical, Cognitive and Social Factors

12. CT - IMAGE DISPLAY Human eye can perceive only a limited range gray-scale values Thoracic image: a) width 400HU/level 40HU (no lung detail is seen) b) width 1000HU/level –700HU (lung detail is well seen; bone and soft tissue detail is lost)

13. Filtering Registration Projects 1&2: Texture-based soft-tissue segmentation Correction Segmentation Projects 3&4: Content-based medical image retrieval and annotation CT Medical Imaging (MI)@ CTI Analysis Visualization Classification Retrieval

14. Outline • Medical Imaging and Computed Tomography • Soft Tissue Segmentation in Computed Tomography • Project 1: Region-based classificationapproach • Project 2: Texture-based snake approach • Content-based Image Retrieval and Annotation • Project 3: Lung Nodule Retrieval based on image content and radiologists’ feedback • Project 4: Associations discovery between image content and radiologists’ assessment

15. Pixel Level Texture Extraction Pixel Level Classification Soft-tissue Segmentation in Computed Tomography Goal: context-sensitive tools for radiology reporting Approach: pixel-based texture classification Organ Segmentation

16. Input Patient Data Characteristics: • hundreds of images per patient • image spatial resolution: 512 x512 • image gray-level resolution: 212 Pixel Level Texture Extraction • Output Data Characteristics: • low-level image features (numerical descriptors) • k=180 Haralick texture features per pixel (9 descriptors x4 directions x5 displacements) Soft-tissue Segmentation in Computed Tomography Pixel-based texture extraction: • Challenges: • Storage: • Input: 0.5+ terabyte of raw data dispersed over about 100K+ images • Output: 90+ terabytes of low-level features in a 180 dimensional feature space • Compute: • 24 hours of compute time = 180 features for a single image on a modern 3GHz workstation

17. Image Partitioning Feature Extraction Stack of CT slices Region Classification Project 1: Challenges and opportunities • Calculate image features at region-level instead of pixel-level • Include Gabor features in the feature extraction phase in addition to the co-occurrence texture features • Explore different approaches for region classification in addition to the decision tree approach Current Implementation: Matlab

18. Original Image Initial Seed at 90% Split & Merge at 85% Split & Merge at 80% Liver Segmentation Example J.D. Furst, R. Susomboon, and D.S. Raicu, "Single Organ Segmentation Filters for Multiple Organ Segmentation",IEEE 2006 International Conference of the Engineering in Medicine and Biology Society (EMBS'06) Region growing at 70% Region growing at 60% Segmentation Result

19. Soft-tissue Segmentation in Computed Tomography Snake Application Demo Next figures are demonstrated how to automatically classify the CT images of heart and liver.

20. Demo For HEART There are 4 main menu to operate this application. SEGMENT: To automatically segment the region of interest organ OPEN: To open a new Image. TEXTURE: To calculate the texture models: co-occurrence/run-length CLASSIFICATION: To automatically classify the segmented organ

21. HEART: Segmentation The application allows users to change Snake/ Active contour algorithm parameters

22. HEART: Segmentation (cont.) Button is clicked User selects points around the region of interest

23. HEART: Segmentation (result) Show segmented organ If the user likes the result of the segmentation, then the user will go to the classification step

24. HEART: Classification Selection of texture models: Co-occurrence, Run-length, Or Combine both models Texture features corresponding to the selected texture model are calculated and shown here

25. HEART: Classification Result Results are shown as follows. Predicted organ: Heart Probability:0.86 And also rule which is used to predict that this segmented organ is HEART

26. Demo For LIVER Start application by open and load the image.

27. LIVER: Segmentation The application allows users to change Snake/ Active contour algorithm parameters

28. LIVER: Segmentation (cont.) Segmentation Button is clicked User selects points around the region of interest

29. LIVER: Segmentation Result Show segmented organ If user is satisfied with the result, then it will go to the classification step

30. LIVER: Classification Select texture models: Co-occurrence, Run-length, Or Combine both models Texture features is calculated for the selected model

31. LIVER: Classification Result Results are shown as follows. Predicted organ: Liver Probability:1.00 And also rule which is used to predict that this segmented organ is LIVER

32. Project 2: Challenges and opportunities • Calculate textureimage features at the pixel level instead of using the gray-levels • Apply snake on the texture features • Investigate different ways to objectively compare two segmentation algorithms, in particular the snake and the classification-based approach Current Implementation: Matlab

33. Outline • Medical Imaging and Computed Tomography • Soft Tissue Segmentation in Computed Tomography • Project 1: Region-based classification approach • Project 2: Texture-based snake approach • Content-based Image Retrieval and Annotation • Project 3: Lung Nodule Retrieval based on image content and radiologists’ feedback • Project 4: Associations discovery between image content and radiologists’ assessment

34. Outline • Medical Imaging and Computed Tomography • Soft Tissue Segmentation in Computed Tomography • Project 1: Region-based classification approach • Project 2: Texture-based snake approach • Content-based Image Retrieval and Annotation • Project 3: Lung Nodule Retrieval based on image content and radiologists’ feedback • Project 4: Associations discovery between image content and radiologists’ assessment

35. Content-based medical image retrieval (CBMS) systems - • Definition of Content-based Image Retrieval: • Content-based image retrieval is a technique for retrieving images on the basis of automatically derived image features such as texture and shape. • Applications of Content-based Image Retrieval: • Teaching • Case-base reasoning • Evidence-based medicine

36. Image Features [D1, D2,…Dn] Feature Extraction Image Database Similarity Retrieval Query Image Feedback Algorithm User Evaluation Query Results Diagram of a CBIR

37. CBIR as a tool for lookup and reference • Case Study: lung nodules retrieval • Lung Imaging Database Resource for Imaging Research http://imaging.cancer.gov/programsandresources/Inf ormationSystems/LIDC/page7 • 29 cases, 5,756 DICOM images/slices, 1,143 nodule images • 4 radiologists annotated the images using 9 nodule characteristics: calcification, internal structure, lobulation, malignancy, margin, sphericity, spiculation, subtlety, and texture • Goals: • Retrieve nodules based on image features: • Texture, Shape, and Size • Find the correlations between the image features and the radiologists’ annotations

38. Examples of nodule images

39. CBIR as a tool for lung nodule lookup and reference Low-level feature extraction:

40. Nodule Characteristics • Calcification • (1. Popcorn, 2. Laminated, 3. Solid, 4. Non-Central, 5. Central, 6. Absent) • Internal Structure • (1. soft tissue, 2. fluid, 3. fat, 4. air) • Subtlety • (1. extremely subtle,..................., 5. obvious) • Sphericity • (1. Linear, 2. ......, 3. Ovoid, 4. ....., 5. Round) • Texture • (1. Non-Solid, 2. ....., 3. Part Solid, 4. ......., 5. Solid)

41. Nodule Characteristics • Margin • (1. Poorly, ......................., 5. Sharp) • Lobulation • (1. Marked, ....................., 5. No Lobulation) • Spiculation • (1. Marked, ....................., 5. No Spiculation) • Malignancy • (1. Highly Unlikely for Cancer, ..............., 5. Highly Suspicious for Cancer)

42. Choose a nodule

43. Choose an image feature& a similarity measure M. Lam, T. Disney, M. Pham, D. Raicu, J. Furst, “Content-Based Image Retrieval for Pulmonary Computed Tomography Nodule Images”, SPIE Medical Imaging Conference, San Diego, CA, February 2007

44. Retrieved Images

45. Project 3: Challenges and opportunities • Calculate co-occurrence texture features at the local level instead of global level • Incorporate shape and size features in the retrieval process in addition to texture features • Integrate radiologists’ assessments/feedback into the retrieval process • Investigate different approaches for retrieval in addition to similarity measures • Report the retrieval results with a certain confidence level (probability) instead of just a binary output (similar/not similar) Current implementation: C# Available Open Source at: http://brisc.sourceforge.net/

46. Outline • Medical Imaging and Computed Tomography • Soft Tissue Segmentation in Computed Tomography • Project 1: Region-based classification approach • Project 2: Texture-based snake approach • Content-based Image Retrieval and Annotation • Project 3: Lung Nodule Retrieval based on image content and radiologists’ feedback • Project 4: Associations discovery between image content and radiologists’ assessment

47. Associations between image content and semantics

48. Project 4: Challenges and opportunities • Investigate other approaches for finding the associations between image features and radiologists’ assessment in addition to logistic regression and decision trees • from image content to semantics • from semantics to semantics • from image features and semantics to semantics • Create GUIs to display examples of images for each semantic concept • Investigate how the current associations discovery approaches apply to mammography assessment (Northwestern project) Current implementation: Matlab, Weka, SPSS

49. Questions? Thank you!