Image Enhancement course: biomedical image processing vibhor kumar Biomedical Image Analysis Rangaraj M. Rangayyan course: biomedical image processing vibhor kumar Hannu Laaksonen

Topics to be covered Convolution mask Operations . unsharp masking . Sobtracting Laplacian 2) High Frequency Emphasis 3) Homomorphic filtering for Enhancement 4) Adaptive Contrast enhancement

Convolution Mask Operators - Unsharp masking The Generalized equation of unsharp masking is f(m,n) = [g(m,n) - µg(m,n)] +  g(m,n) blurred image Is calculated as average of the pixels in the window taken around the pixel(m,n) The weight  can be changed according to desired effect. For e.g. For a 3X3 convolution mask the unsharp masking is given by - 1 8 2

Subtracting Laplacian The degraded image may be expressed in a Taylor series as g t 2 2 t2 2g g(x,y,) = g(x,y,0) +  (x,y,t) - + …... g t Taking = k2g using diffusion model We get fe = g - k2g For k = 1 and mask 3X3 we get subtracting Laplacian as: 0 -1 0 -1 5 -1 Unlike Laplacian subtracting Laplacian maintain the intensity information while making the image sharp

(a) (b) original lena image Laplacian Unsharp masking subtracting laplacian (d) (c)

High-frequency Emphasis Highpass filtering are useful in detecting edges but for enhancing the images it is necessary to maintain the intensity information. High-emphasis filter does the image enhance keeping the intensity information. The Butterworth high-emphasis filter can termed as: 2 D0 D(u,v) 2n 1 + (sqrt(2)-1 ) 1 + H(u,v) = Filter gain Frequency

Original shape image the ideal high pass filter The Butter worth highpass filter the Butterworth high-emphasis filter

Enhancement using Homomorphic filtering linear filtering and enhancement inverse transform transform input image filtered image

Original Image log transform of original image Homomorphic filtering including a Butterworth high-emphasis filter Butterworth high imphasis filter only Graphical Models and Image Processing, 54(3):259-267,May 1992

Adaptive Contrast Enhancement Adaptive-neighborhood contrast enhancement: non overlapping regions segmentation Overlapping regions segmentation seed fill region growing: The region consists of spatially connected pixels that falls that fall within the specified gray level devaiation from seed pixel. every time data is devided into back ground and foreground pixels The growth tolerance threshold  is highly important factor

Adaptive Contrast Enhancement contrast enhancment can be done using the formula fe = b (1+Ce)/ (1-Ce) increased contrast mean background value Part of mammogram with a cluster of calcification adaptive-neighborhood contrast enhancement gamma correction unsharp masking IEEE Transcation on Medical imaging 11(3):392-406,1992

Topics covered Convolution mask Operations . unsharp masking . Sobtracting Laplacian 2) High Frequency Emphasis 3) Homomorphic filtering for Enhancement 4) Adaptive Contrast enhancement