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The University of Ontario CS 4487/9587 Algorithms for Image Analysis Image Modalities most slides are shamelessly stolen from Steven Seitz, Aleosha Efros,

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Presentation on theme: "The University of Ontario CS 4487/9587 Algorithms for Image Analysis Image Modalities most slides are shamelessly stolen from Steven Seitz, Aleosha Efros,"— Presentation transcript:

1 The University of Ontario CS 4487/9587 Algorithms for Image Analysis Image Modalities most slides are shamelessly stolen from Steven Seitz, Aleosha Efros, and Terry Peters

2 The University of Ontario CS 4487/9587 Algorithms for Image Analysis Image Modalities n Photo/Video data Pin-hole Lenses Digital images and volumes n Medical Images and Volumes X-ray, MRI, CT, and Ultrasound Extra Reading: Forsyth & Ponce, Ch. 1. Gonzalez & Woods, Ch. 1

3 The University of Ontario Slide by Steve Seitz How do we see the world? n Let’s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image?

4 The University of Ontario Slide by Steve Seitz Pinhole camera n Add a barrier to block off most of the rays This reduces blurring The opening known as the aperture How does this transform the image?

5 The University of Ontario Slide by Steve Seitz Camera Obscura n The first camera Known to Aristotle Depth of the room is the focal length Pencil of rays – all rays through a point Can we measure distances?

6 The University of Ontario Figure by David Forsyth Distant objects are smaller

7 The University of Ontario Slide by Aleosha Efros Camera Obscura Drawing from “The Great Art of Light and Shadow “ Jesuit Athanasius Kircher, How does the aperture size affect the image?

8 The University of Ontario Slide by Steve Seitz Shrinking the aperture n Why not make the aperture as small as possible? Less light gets through Diffraction effects…

9 The University of Ontario Slide by Steve Seitz Shrinking the aperture

10 The University of Ontario Slide by Aleosha Efros Home-made pinhole camera

11 The University of Ontario Slide by Steve Seitz The reason for lenses

12 The University of Ontario Slide by Steve Seitz Adding a lens n A lens focuses light onto the film There is a specific distance at which objects are “in focus” –other points project to a “circle of confusion” in the image Changing the shape of the lens changes this distance “circle of confusion”

13 The University of Ontario Slide by Aleosha Efros The eye n The human eye is a camera! Iris - colored annulus with radial muscles Pupil - the hole (aperture) whose size is controlled by the iris What’s the “film”? –photoreceptor cells (rods and cones) in the retina

14 The University of Ontario Slide by Aleosha Efros Cameras n Really cool n Not too expensive nowadays (<$200) Canon A70

15 The University of Ontario Basic camera model image plane lens f “equivalent” image plane f

16 The University of Ontario Basic camera model “virtual” image plane camera’s “optical center” f commonly used simplified representation of a photo camera

17 The University of Ontario Figure by Gonzalez & Woods Digital Image Formation f(x,y) = reflectance(x,y) * illumination(x,y) Reflectance in [0,1], illumination in [0,inf]

18 The University of Ontario Figure by Gonzalez & Woods Sampling and Quantization

19 The University of Ontario Figure by Gonzalez & Woods Sampling and Quantization

20 The University of Ontario Slide by Aleosha Efros What is an image? n We can think of an image as a function, f, from R 2 to R: f( x, y ) gives the intensity at position ( x, y ) Realistically, we expect the image only to be defined over a rectangle, with a finite range: –f : [a,b] x [c,d]  [0,1] n A color image is just three functions pasted together. We can write this as a “vector-valued” function:

21 The University of Ontario Slide by Aleosha Efros Images as functions

22 The University of Ontario Slide by Aleosha Efros What is a digital image? n We usually operate on digital (discrete) images: Sample the 2D space on a regular grid Quantize each sample (round to nearest integer) If our samples are  apart, we can write this as: f[i,j] = Quantize{ f(i , j  ) } n The image can now be represented as a matrix of integer values

23 The University of Ontario 3D Image Volumes? n Video - 3D = X*Y*Time n Medical volumetric data (MRI, CT) - 3D = X*Y*Z k Combine multiple images (slices) into a volume

24 The University of Ontario CS 4487/9587 Algorithms for Image Analysis Image Modalities X-ray CT MRI Ultrasound PART II: Medical images and volumes

25 The University of Ontario Slides from Terry Peters In the beginning…..X-rays n Discovered in 1895 n Mainstay of medical imaging till 1970’s n 1971 – Computed Tomography (CAT, CT) scanning n Digital Radiography ……… n 1980 Magnetic Resonance Imaging n Discovered in 1895 n Mainstay of medical imaging till 1970’s n 1971 – Computed Tomography (CAT, CT) scanning n Digital Radiography ……… n 1980 Magnetic Resonance Imaging

26 The University of Ontario X-rays n Wilhelm Conrad Röntgen ( ) Nobel Prise in Physics, 1901 “X” stands for “unknown” X-ray imaging is also known as - radiograph - Röntgen imaging

27 The University of Ontario X-rays Bertha Röntgen’s Hand 8 Nov, 1895 A modern radiograph of a hand Calcium in bones absorbs X-rays the most Fat and other soft tissues absorb less, and look gray Air absorbs the least, so lungs look black on a radiograph

28 The University of Ontario X-rays 2D “projection” imaging ’s

29 The University of Ontario From Projection Imaging Towards True 3D Imaging Mathematical results: Radon transformation 1917 Computers can perform complex mathematics to reconstruct and process images Late 1960’s: X-ray imaging 1895 Development of CT (computed tomography) 1972 Image reconstruction from projection Also known as CAT (Computerized Axial Tomography) "tomos" means "slice" (Greek)

30 The University of Ontario Radon Transformation Mathematical transformation (related to Fourier) Reconstruction of the shape of object (distribution f(x,y)) from the multitude of 2D projections

31 The University of Ontario Figure from CT imagingwww.imaginis.com/ct-scan/how_ct.asp

32 The University of Ontario CT imaging, inventing (1972) n Sir Godfrey Hounsfield Engineer for EMI PLC 1972 n Nobel Prize 1979 (with Alan Cormack)

33 The University of Ontario CT imaging, availability (since 1975) The EMI-Scanner Original axial CT image from the dedicated Siretom CT scanner circa This image is a coarse 128 x 128 matrix; however, in 1975 physicians were fascinated by the ability to see the soft tissue structures of the brain, including the black ventricles for the first time (enlarged in this patient) 1974 Axial CT image of a normal brain using a state-of-the-art CT system and a 512 x 512 matrix image. Note the two black "pea-shaped" ventricles in the middle of the brain and the subtle delineation of gray and white matter (Courtesy: Siemens) 25 years later

34 The University of Ontario Slides from Terry Peters Clinical Acceptance of CT!? n Dr James Ambrose 1972 Radiologist, Atkinson - Morley’s Hospital London Recognised potential of EMI- scanner n “Pretty pictures, but they will never replace radiographs” – Neuroradiologist 1972

35 The University of Ontario Slides from Terry Peters Then ……………and Now n 80 x 80 image n 3 mm pixels n 13 mm thick slices n Two simultaneous slices!!! n 80 sec scan time per slice n 80 sec recon time n 512 x 512 image n <1mm slice thickness n <0.5mm pixels n 0.5 sec rotation n 0.5 sec recon per slice n Isotropic resolution n Spiral scanning - up to 16 slices simultaneously

36 The University of Ontario Slides from Terry Peters 30 Years of CT

37 The University of Ontario Slides from Terry Peters Birth of MRI n Paul Lautebur 1975 Presented at Stanford CT meeting “Zeugmatography” n Raymond Damadian 1977 – n Sir Peter Mansfield early 1980’s Early Thorax Image Nottingham

38 The University of Ontario Slides from Terry Peters Birth of MRI Early Thorax Image Nottingham Electro Marnetic signal emitted (in harmless radio frequensy) is acquired in the time domain image has to be reconstructed (Fourier transform)

39 The University of Ontario Birth of MRI In 1978, Mansfield presented his first image through the abdomen. Lauterbur and the first magnetic resonance images (from Nature)

40 The University of Ontario Slides from Terry Peters 30 Years of MRI First brain MR image Typical T2-weighted MR image

41 The University of Ontario Slides from Terry Peters MR Imaging n “Interesting images, but will never be as useful as CT” (A different) neuroradiologist, 1982

42 The University of Ontario Slides from Terry Peters MR Imaging …more than T1 and T2 n MRA - Magnetic resonance angiography images of vessels n MRS - Magnetic resonance spectroscopy images of chemistry of the brain and muscle metabolism n fMRI - functional magnetic resonance imaging image of brain function n PW MRI – Perfusion-weighted imaging n DW MRI – Diffusion-weighted MRI images of nerve pathways

43 The University of Ontario Slides from Terry Peters Magnetic Resonance Angiography n MR scanner tuned to measure only moving structures n “Sees” only blood - no static structure n Generate 3-D image of vasculature system n May be enhanced with contrast agent (e.g. Gd-DTPA)

44 The University of Ontario Slides from Terry Peters MR Angiography Phase-contrast In-flow GD-enhanced

45 The University of Ontario 1 Slides from Terry Peters Dynamic 3-D MRI of the thorax

46 The University of Ontario Slides from Terry Peters Diffusion-Weighted MRI n Image diffuse fluid motion in brain n Construct “Tensor image” – extent of diffusion in each direction in each voxel in image n Diffusion along nerve sheaths defines nerve tracts. n Create images of nerve connections/pathways

47 The University of Ontario n Data analysed after scanning n Identify “streamlines” of vectors n Connect to form fibre tracts n 14 min scan time - Dr. D Jones, NIH Internal Capsule Slides from Terry Peters Tractography

48 The University of Ontario Slides from Terry Peters Tractography Superior Longitudinal Fasciculus Short fibres Temporal fibres Insula fibres Wernicke’s area Broca’s area Long fibres - Dr. D Jones, NIH USA “just like Gray’s Anatomy”!

49 The University of Ontario Slides from Terry Peters Functional MRI (fMRI) n Active brain regions demand more fuel (oxygen) n Extra oxygen in blood changes MRI signal n Activate brain regions with specific tasks n Oxygenated blood generates small (~1%) signal change n Correlate signal intensity change with task n Represent changes on anatomical images

50 The University of Ontario Slides from Terry Peters fMRI Subject looks at flashing disk while being scanned “Activated” sites detected and merged with 3-D MR image Stimulus Activation

51 The University of Ontario Hand Activation Face Activation Tumour Slides from Terry Peters fMRI in Neurosurgery Planning

52 The University of Ontario Slides from Terry Peters Ultrasound Images courtesy GE Medical


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