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Elements of Biomedical Image Processing BMI 731 Winter 2005. Kun Huang Department of Biomedical Informatics Ohio State University. Introduction to imaging processing Mathematical background Convolution and Fourier transform Filtering Image enhancement Noise removal

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elements of biomedical image processing bmi 731 winter 2005

Elements of Biomedical Image ProcessingBMI 731 Winter 2005

Kun Huang

Department of Biomedical Informatics

Ohio State University

slide2

Introduction to imaging processing

  • Mathematical background
    • Convolution and Fourier transform
    • Filtering
  • Image enhancement
    • Noise removal
    • Color correction and color space transform
  • Feature extraction
    • Edge, point, line (Hugh transform)
  • 3-D reconstruction
    • Radon transform
slide3

Image Processing : what should be done?

    • Image restoration and enhancement
    • Feature extraction
    • Pattern recognition
slide4

1x2+8x9+15x4+7x7+14x5

+16x3+13x6+20x1+22x8

=575

  • Mathematical Background
    • Convolution
    • 2-D convolution
slide5

Mathematical Background

    • Fourier transform (FT)
      • Mathematics
      • 2-D FT
slide6

Mathematical Background

    • Fourier transform (FT)
      • Fast FT (FFT)
slide7

Mathematical Background

    • Convolution and Fourier transform (FT)
slide8

Mathematical Background

    • Filtering
      • High-pass filter, low-pass filter, band pass filter
      • Gradient filters
slide9

Mathematical Background

    • Filtering
      • Wiener filter and deblurring
slide10

43

  • Image Enhancement
    • Denoise
      • Averaging
      • Median filter
slide11

Image Enhancement

    • Denoise/restoration

From Gonzalez, Woods, and Eddins

slide12

Image Enhancement

    • Color and intensity adjustment
      • Histogram equalization
slide13

RGB -> HSV, HSL, YCbCr, …

R = 64

G = 31

B = 62

R = 125

G = 80

B = 147

H = 214

S = 132

V = 64

H = 199

S = 117

V = 147

  • Image Enhancement
    • Color space transform
slide14

Feature Extraction

    • Region detection – morphology manipulation
      • Dilate and Erode
      • Open
        • Erode  dilate
        • Small objects are removed
      • Close
        • Dilate  Erode
        • Holes are closed
      • Skeleton and perimeter
slide15

Feature Extraction

    • Edge detection
      • Gradients
      • Canny edge detector
        • Gaussian smoothing
        • Gradients
        • Two thresholds
        • Thinning
slide16

x

  • Feature Extraction
    • Point detection
      • Harris detector
slide17

y

q

y

q

  • Feature Extraction
    • Radon transform
    • Straight line detection
      • Hugh transform
slide18

From Gonzalez, Woods, and Eddins

  • Feature Extraction
    • Straight line detection
      • Hugh transform
slide19

2-D/3-D reconstruction

    • Radon/inverse radon transforms and backprojection
slide20

Reference

    • Digital Image Processing using Matlab

By R.C.Gonzalez, R.E.Woods, and S.L.Eddins

Published by Printice-Hall, 2004