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Advanced Image Processing Techniques in Biomedical Engineering

This content outlines essential concepts in biomedical image processing, including methods for image display, linear systems, and various imaging techniques such as optical microscopy, X-ray, magnetic resonance imaging (MRI), ultrasound, and electron microscopy. Key topics include pixel representation, greyscale and pseudo-colour display, contrast enhancement, and the impact of median filtering on image quality. Additionally, it covers machine detection principles, Rose's criterion, and the convolution integral within linear systems, providing a comprehensive overview for students and professionals in biomedical engineering.

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Advanced Image Processing Techniques in Biomedical Engineering

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  1. BME1450 – Biomedical Engineering 2002 Basic Image Display and Processing Linear Systems and Imaging Optical Microscopy X-ray Magnetic Resonance Imaging Ultrasound Nuclear Medical Electron Microscopy Scanning Probe Microscopy BME 1450 - 2002

  2. Basic Image Display Greyscale • Measurements displayed as colors or shades of gray. • Pixels or voxels. • Axes unusual • Colour maps BME1450 - 2002

  3. Basic Image DisplayPseudo Colour • Max 105 :white • Min 0 :black • (R, G, B) • Contrast • Histogram • 4x104 :white • 2.5x104 :black BME1450 - 2002

  4. Basic Image DisplayContrast enhanced • Note colour bar range 2.5e4 to 4e4. • Higher contrast • More noise BME1450 - 2002

  5. Basic Image DisplayMedian Filtered • Median Filter • Less spatial resolution BME1450 - 2002

  6. Basic Image DisplayNoise • Find the disk objects. • 32x32 pixels • R = 3 pixels • 100 photons /pixels • + - 10 • Poisson BME1450 - 2002

  7. Basic Image DisplayRose’s Criterion • Rose’s hypothesis is that the human will do as well as the best machine when both human and machine are given the same task and a priori information. • N equals sum of the pixels in the region where the disk may be. • The machine knows average N = NBG if the disk is present and NOB if it is absent. BME1450 - 2002

  8. Basic Image DisplayMachine Detection • N equals sum of the pixels in the region where the disk may be. • The machine knows average N = NBG if the disk is present and NOB if it is absent. • The machine says "present" if N > (NOB + NBG)/2 and "absent" otherwise. • I chose cases for which the machine's probability of false positive is the same. BME1450 - 2002

  9. Basic Image DisplayEqually Visible? • Now R = 7 pixels. • Disks have same visibility to the machine. • Their contrast is reduced. • Are they as easy to spot as the R=3? BME1450 - 2002

  10. BME1450 – Biomedical Engineering 2002 Basic Image Display and Processing Linear Systems and Imaging Optical Microscopy X-ray Magnetic Resonance Imaging Ultrasound Nuclear Medical Electron Microscopy Scanning Probe Microscopy BME 1450 - 2002

  11. Linear Systems and ImagingLinearity • Linear BME1450 - 2002

  12. Linear Systems and ImagingSpatial Invariance • Spatially Invariant BME1450 - 2002

  13. Linear Systems and ImagingConvolution Integral The convolution integral • h(x,y) is the “point response function” • It is the image of a delta function. • It is translated by (a,b), weighed by f(a,b) and added to g(x,y). BME1450 - 2002

  14. Linear Systems and ImagingFourier Theory • Fourier Transform • Convolution Theorem • H is the transfer function of the system BME1450 - 2002

  15. Fourier Domain = K Space BME1450 - 2002

  16. Linear Systems and Imaging Effects of filtering Filter Width 32 pix In K space Of 256x256 BME1450 - 2002

  17. Linear Systems and ImagingSpectral Components • Real part of the FFT of pixel Kx,Ky =3 • Other pixels 0 BME1450 - 2002

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