Automatic Minirhizotron Root Image Analysis Using Two-Dimensional

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 Minirhizotron Soil Sampling Rhizotron

4. Previous work on minirhizotron image analysis • [Vamerali & Ganis 1999] Nonlinear contrast stretching technique is used to enhance the local contrast of rootsLimitation: The minimum root length filter will eliminate some shorter roots. • [Natar & Baker 1992] An artificial neural system is developed to identify rootsLimitation: 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 rootsLimitation: Only works for a single type of root.

5. Preview of Experimental Results Original image Extracted root Measured root

6. Approach Overview

7. Image Preprocessing 1. Conversion to grayscale Color Red Green Blue 2. Contrast stretching We set 3. Smoothing the image

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

9. Matched Filtering Procedure • A number of cross sections of identical profiles are matched • simultaneously. A kernel can be used which mathematically • expressed as: for |y| ≤ L/2 where L is the length of the segment for which the root is assumed to have a fixed orientation. • 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.

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

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 pij 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. Local Entropy Thresholding (cont.) 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 HT(t) gives the optimal threshold for object-background classification.

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

16. Selecting the Root 1. Connected component labeling 2. Root candidate selecting (Ai ≥ 0.8 Amax )

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

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

19. Root Measurement (cont.) 3. Estimating the length Freeman formula Pythagorean theorem Kimura’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 False positives are caused by 1. A bright extraneous object 2. Uneven diffusion of light through the minirhizotron wall

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

23. Root Discrimination: Five Methods (cont.) 4. Histogram Distribution 5. 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 image Extracted root Measured root

26. Experimental Results (cont.) Original image Extracted root Measured root

27. Experimental Results (cont.) Original image Extracted root Measured 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 • 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: • Works on some images, but the false positive rate is increased to 14% (more bright background objects are misclassified).

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