1. Automatic Minirhizotron Root Image Analysis Using Two-Dimensional Matched Filtering and Local Entropy Thresholding Presented by Guang Zeng

2. Importance of Studying Roots

3. Methods for Studying Roots Soil SamplingRhizotron Minirhizotron

4. Previous work on minirhizotron image analysis [Vamerali & Ganis 1999] Nonlinear contrast stretching technique is used to enhance the local contrast of roots Limitation: The minimum root length filter will eliminate some shorter roots. [Natar & Baker 1992] An artificial neural system is developed to identify roots Limitation: The accuracy will substantial decrease when applied to images that have not been trained. [Dowdy & Smucker 1998] The length-to-diameter ratio is used to discriminate roots Limitation: Only works for a single type of root.

5. Preview of Experimental Results Original imageExtracted rootMeasured root

6. Approach Overview

7. Image Preprocessing 1. Conversion to grayscale 2. Contrast stretching 3. Smoothing the image RedGreenBlueColor We set

8. Matched Filtering Principles Similarity between plant roots and blood vessels: Small curvature Parallel edges Young roots appear brighter Gaussian curve for gray level profile of cross section: Motivation:  piecewise linear segments [Chaidhuri et. 1989]

9. Matched Filtering Procedure A number of cross sections of identical profiles are matched simultaneously. A kernel can be used which mathematically expressed as: Kernels, for which the mean value is positive, are forced to have slightly negative mean values in order to reduce the effect of background noise. for |y| ≤ L/2 where L is the length of the segment for which the root is assumed to have a fixed orientation.

10. Matched Filtering Procedure (cont.) The kernel is rotated using an angular resolution of 15° (12 kernels are needed to span all possible orientations). The kernel is applied at two scales (full image size and half image size, obtained by subsampling). (a) 15°(b) 75°(c) 135°(d) 180°

11. Matched Filtering Output (a) 75°(b) 90° ( c) 135°(d) 180°

12. Local Entropy Thresholding Shannon’s entropy and where,

13. Local Entropy Thresholding (cont.) The probability of co-occurrence p ij of gray levels i and j can therefore be written as: Divide co-occurrence matrix into quadrants, using threshold t (0 ≤ t ≤ L) The local entropy is defined by the quadrants A and D.

14. Background-to-background entropy: Foreground-to-foreground entropy: Hence, the total second-order local entropy of the object and the background can be written as: The gray level corresponding to the maximum of H T (t) gives the optimal threshold for object-background classification. Local Entropy Thresholding (cont.)

15. Local Entropy Thresholding Outputs t = 122t = 103t = 155t = 130 (a) 75°(b) 90°(c) 135°(d) 180°

16. Selecting the Root 1. Connected component labeling 2. Root candidate selecting (A i ≥ 0.8 A max )

17. Comparison of Root Selection Methods original image combined MF output [Chanwinmaluang and Fan 2003] Our method detected root... separate MF outputs

18. Root Measurement 1. Object Skeletonization 2. Extracting medial line using Dijkstra’s Algorithm

19. Root Measurement (cont.) 3. Estimating the length Freeman formulaPythagorean theoremKimura’s method

20. Root Measurement (cont.) 4. Estimating the average diameter Step 1 Select 10 nodes that equally divide the medial line into 11 parts. Step 2 Find the corresponding opposite boundary point pairs, calculate the distance between each opposite boundary point pairs. Step 3 Discard the two pairs that yield the maximum and the minimum distance

21. Root Discrimination 1. A bright extraneous object 2. Uneven diffusion of light through the minirhizotron wall False positives are caused by

22. Root Discrimination: Five Methods 1. Eccentricity e = c / a 2. Approximate line symmetry 3. Boundary parallelism

23. Root Discrimination: Five Methods (cont.) 4. Histogram Distribution5. Edge Detection

24. Experimental Results We tested our method on a set of 45 minirhizotron images containing different sizes of roots different types of roots dead roots no roots The output of the algorithm is compared with hand-labeled ground truth provided by the Clemson Root Biology Lab.

25. Experimental Results (cont.) Original imageExtracted rootMeasured root

26. Experimental Results (cont.) Original imageExtracted rootMeasured root

27. Experimental Results (cont.) Original imageExtracted rootMeasured root

28. Comparison of Root Length Measurement Methods 1. Measurement Deviation 2. Correlation

29. Comparison of Root Discrimination Methods 1. The optimal threshold point is the closest point to the perfect result. The closer the optimal threshold point to the point (0,1), the more accurate the method. 2. The larger the area beneath an ROC curve, the more accurate the method.

30. Multiple root detection Works on some images, but the false positive rate is increased to 14% (more bright background objects are misclassified). Our technique is limited to zero or one root per image. We tried detecting multiple roots by extracting the two largest components in the thresholded binary images, then running our algorithm. Some results:

31. Conclusion Fully automatic algorithm for detecting and measuring roots Works on multiple root types Uses individual matched filters outputs, without first combining them. Uses a robust thresholding method Robust medial line detection using Dijkstra’s algorithm Proposed five different methods for root / no-root discrimination Future work 1. Accurate multi-root detection 2. Reducing the computation time