1. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 1 Medical Imaging and Pattern Recognition Lecture 5 Image Measurements and Operations Oleh Tretiak

2. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 2 Previous Lectures Images represent objects. We wish to see or measure items in images. Image quality is affected by resolution and sampling. Image quality is affected by noise. Images are stored in files, and are displayed with pixels. They have grey scale.

3. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 3 This Lecture Image enhancement –Seeing more in images Image measurement –Data from images

4. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 4 Image Enhancement Contrast operations Image scaling, rotation Filtering –Noise reduction –Edge enhancement Image subtraction Image addition Image stacks

5. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 5 Contrast Operations I(x,y) - initial image; O(x,y) - output image; At each x,y point: O(x, y) = cI(x,y)+b c ~ contrast, b ~ brightness I(x, y)O(x, y)

6. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 6 Histogram I(x, y) image, 0≤I(x,y)≤k integer values only H(j), 0≤j≤k histogram of image I. H(j) = number of pixels for which I(x, y) = j

7. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 7 Contrast Control Features ImageJ Brightness & Contrast Control “Historgram Equalization” An automatic contrast adjustment method.

8. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 8 Image Scaling, Rotation, Cropping Original, 128x128 Rotate by 15° Scale by 2, 256x256 Scale by 0.5, 64x64, less information.

9. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 9 What is Image Filtering? Compute new image from old image Each pixel in new image depends on ‘nearby’ pixels in old image For all x, y O(x, y) = F(I(x, y), I(x+1, y), I(x-1, y), I(x, y+1), I(x, y-1))

10. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 10 Examples-Formulas Examples –O(x, y) = {I(x, y)+I(x+1, y)+I(x-1, y)+I(x, y+1)+I(x, y-1)}/5 local average –O(x, y) = {4I(x, y)-I(x+1, y)-I(x-1, y)-I(x, y+1)-I(x, y-1)} Laplacian –O(x, y) = {I 2 (x, y)+I 2 (x+1, y)+I 2 (x-1, y)+I 2 (x, y+1) +I 2 (x, y-1)}/5 Quadratic (nonlinear) Output depends on neighboring pixels of the input function Neighborhood can be small or large

11. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 11 Linear Low-Pass Filter Use: Noise reduction –ImageJ function Process->Smooth

12. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 12 Median Filter Use: Noise reduction –ImageJ: Process->Filters->Median...

13. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 13 Find Edges ImageJ: Process->Find Edges

14. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 14 Image Operations Unary operations (Process->Math->...) –O(x, y) = log(I(x, y)) –O(x, y) = 1/I(x, y) Binary operations with constant (Process->Math->...) –O(x, y) = I(x, y) + a –O(x, y) = I(x, y)*b Binary operations with images (Process->Image Calculator) –O(x, y) = I 1 (x, y) + I 2 (x, y) –O(x, y) = I 1 (x, y) – I 2 (x, y)

15. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 15 Example: Image Subtraction Slice 22Slice 2322 - 23

16. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 16 Stacks Stacks are sequences of images, all have the same dimensions Most often, a stack is a sequence of slices through a 3-D object May be a time sequence (movie)

17. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 17 Example: Stack Converted to Montage

18. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 18 Image Measurements Types of Measurement –Geometry –Intensity ROI (region of interest) Segmentation

19. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 19 Why Measure? Numbers are inputs to decisions Example of geometric measurement: Area Example of intensity measurement: amount of DNA

20. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 20 Measurements in ImageJ Area Bounding Rectangle Circularity Centroid Perimeter Fit Ellipse Feret’s Diameter Grey value standard deviation Min & Max Grey Value Center of Mass Mean Gray Value Modal Gray Value

21. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 21 Example of Measurements Area 16384 Mean 158.431 StdDev 57.680 Mode 194 Min 0 Max 255 X 64 Y 64... 128x128

22. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 22 How to Measure Head Only? Specify region of interest (ROI) Measure within ROI Method of specifying ROI –Manual –Automatic –Combination

23. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 23 Manual Selection Tools Rectangle Oval Polygon Freehand Oval selection tool. Area 5626, Mean 97.139,....

24. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 24 Semi-Automatic Selection Use Image->Adjust->Set Threshold to specify gray value to be measured Use magic wand to select objects Measure the objects

25. Image with threshold setting Selection made with magic wand

26. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 26 Analyze Particles Command Find a point in image using specified threshold Trace outline with magic wand Measure interior Clear selection Continue until done

27. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 27

28. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 28 Example Images have been analyzed. 27 sets of measurements have been recorded.

29. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 29 Image Processing Methods Image enhancement Image measurement –Image enhancement can be a preliminary step to measurement Important image enhancement –Contrast adjustment –Image scaling

30. MIPR Lecture 5 Copyright Oleh Tretiak, 2004 30 Image Measurements Specify ROI (region of interest, selection) Measure –Geometric features –Gray level features Manual ROI is reliable Automatic ROI is sometimes possible