automatic minirhizotron root image analysis using two-dimensional matched filtering and local...
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Automatic Minirhizotron Root Image Analysis Using Two-Dimensional
Matched Filtering and Local Entropy Thresholding
Presented by Guang Zeng
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 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.
Image Preprocessing1. Conversion to grayscale
2. Contrast stretching
3. Smoothing the image
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Matched Filtering PrinciplesSimilarity between plant roots and blood vessels:
• Small curvature
• Parallel edges
• Young roots appear brighter
• Gaussian curve for gray level profile of
cross section:
)}2
dexp(k1{A)y,x(f
2
2
Motivation:
piecewise linear segments[Chaidhuri et. 1989]
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.
)2
xexp()y,x(K
2
2
for |y| ≤ L/2
where L is the length of the segment for which the root is assumed to have a fixed orientation.
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°
Local Entropy ThresholdingShannon’s entropy
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n
1ii
and
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i
n
ii pp
M
l
N
kij klt
1 1
),(
otherwisekl
jklfandiklf
or
jklfandiklf
ifkl
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where,
Local Entropy Thresholding (cont.)
The probability of co-occurrence pij of gray levels i and j can
therefore be written as:
i jij
ijij t
tp
Divide co-occurrence matrix into quadrants, using threshold t (0 ≤ t ≤ L)
The local entropy is defined by the quadrants A and D.
Background-to-background entropy:
Aij2
t
0i
t
0j
AijA PlogP
2
1)t(H
Foreground-to-foreground entropy:
Dij
L
1ti
L
1tj2
DijD PlogP
2
1)t(H
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.
)t(H)t(H)t(H DAT
Local Entropy Thresholding (cont.)
Comparison of Root Selection Methods
originalimage
combined MF output
[Chanwinmaluang and Fan 2003]
Our method
detected root detected root
...
separate MF outputs
Root Measurement (cont.)3. Estimating the length
Freeman formula
od NN2L Pythagorean theorem
2/12od
2d ])NN(N[L
Kimura’s method2/N])2/NN(N[L o
2/12od
2d
Root Measurement (cont.)
4. Estimating the average diameter
Step 1Select 10 nodes that equally divide the medial line into 11 parts.
Step 2Find the corresponding opposite boundary point pairs, calculate the distance between each opposite boundary point pairs.
Step 3Discard the two pairs that yield the maximum and the minimum distance
Root Discrimination
1. A bright extraneous object
2. Uneven diffusion of light through the minirhizotron wall
False positives are caused by
Root Discrimination: Five Methods
1. Eccentricity
e = c / a
2. Approximate line symmetry
3. Boundary parallelism
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.
Comparison of Root Length Measurement Methods
1. Measurement Deviation
2. Correlation
Measurement Devi ati on (%) Freeman Formul a Ki mura' s Method
Max 17.99 14.42
Mi n 0.296 0.074
Avg 7.99 4.56
Comparison of Root Discrimination Methods1. 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.
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:
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
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