DT-Binarize - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 159

Table 2
Comparison of DT-Binarize and max Limit

Max Limit DT-Binarize

Acc 0.987 0.9844

F1 0.852 0.8106

MCC 0.86 0.8236

Jacc 0.771 0.7103

5 Conclusion
This paper presents a new technique for image binarization problem using a group of different
thresholding methods. DT-Binarize is a supervised method with training and testing stages.
In the training stage, a decision tree is built using the standard deviation of the protein images.
Leaf nodes of the tree represent different thresholding techniques that provide the best binar-
ization method for a specific group of images. In the testing stage, using the decision tree, we
select the best thresholding technique for the test sample and then generate the binary image
using that technique.
We evaluate the performance of our approach with four different accuracy measures. For
all cases, DT-Binarize outperformed other single thresholding methods. Experimental results
show that our technique improves the binarization accuracy by 10% on the average and
provides high accuracy by reaching 95% of the expert choices.

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