1) The document presents a method for classifying burn images using a one-class support vector machine (SVM). Burn images are first acquired and preprocessed before feature extraction. 2) An adaptive one-class SVM classifier is used to identify the degree of burn based on whether the images fall inside or outside the SVM boundary. This allows burn images of uncertain classification to be raised to a higher severity level based on expert feedback. 3) The experimental results on a dataset from Cho Ray Hospital show that the adaptive one-class SVM approach is effective for classifying burn images into different severity levels. Future work will focus on improving classification accuracy and expanding the approach to more burn severity levels.

1. International Conference on Context-Aware Systems and
Applications ICCASA 2015
BURN IMAGE CLASSIFICATION USING ONE-CLASS
SUPPORT VECTOR MACHINE
Author: Hai Tran
Triet Le
Thai Le
Thuy Nguyen

2. Agenda
Introduction1
Burn Image Classification2
Applying SVM for Burn Image Classification3
Conclusion and Future Work4
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3. IMAGE CLASSIFICATION INTRODUCTION
Image Classification
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Class 1
Class 2
…
Class L
Classifier

4. IMAGE CLASSIFICATION INTRODUCTION
Image classification process
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Input
Image
Pre-
processing
Feature
Extraction
Classifier
Result

5. IMAGE CLASSIFICATION INTRODUCTION
Image Classification Approaches
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Images
Classification
K-Nearest
Neighbor
(K-NN)/ K-Means
LDA AdaBoost
Atificial Neural
Network
(ANN)
Support Vector
Machines (SVM)

6. IMAGE CLASSIFICATION
Image classification system using SVM
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Image classification system using SVM

7. Burn Images
 4 degrees of burn:
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* The Journal of the Ameriacan Medical Association (Nov. 2014),
http://jama.jamanetwork.com/

8. Burn Image Classification
 Illustration for degrees burning images
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9. Burn Image Classification process
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 Step 1: Burn Image Acquistion
 Step 2: Drop to standarize the size to segmentation
 Step 2: Feature extraction
 Step 4: Using SVM classifier to identify the degree of
burn

10. Burn Image Classification
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11. Multi classes SVM

12. Adaptive SVM classifier for burn images
{x|(wT.x)+b= -1} {x|(wT.x)+b= +1}
{x|(wT.x)+b=0}
+
++
+
+
+
+
+
+
-
-
-
-
-
-
-
-
-
{x|(wT.x)+b= -1} {x|(wT.x)+b= +1}
{x|(wT.x)+b=0}
N/A
o
o o
o
o
 In case difficult to identify belong
class II or class III or N/A. Based on
expert suggestion, the computer
system should raise the high level.
Traditional Adaptive
+
++
+ +
+
+
-
-
-
-
-
-

13. Some Training Images
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14. Some Testing Images
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15. Experimental Results on Cho Ray supplying Dataset
http://fit.hcmup.edu.vn/medical_image_project/

16. COCLUSION AND FUTURE WORK
1. Conclusion
- Researching and selecting the suitable SVM Classifier for
burn image classification.
- Adjust OAA strategy of SVM for burn image
classification.
2. Future Work
- Improving the accuracy of SVM classification model for
burn images.
- Increase the number of classes.
- Apply on the bigger data.
ICCASA 2015

17. REFERENCES
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3. Survana, M., Sivakumar Niranjan, U. C.: Classification methods of skin burn images.
IJCSIT (2013)
4. Acha, B., Serrano, C., and Laura M.R: Segmentation and classification of burn images by color and texture information.
Journal of biomedical optics 10.3 (2005)
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7. Tam, T. D., and Binh, N. T.: Efficient Pancreas Segmentation in Computed Tomography
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18. Thank You
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