Classification, Principles, assessment and management of burn
Burn image classification using support vector machine
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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
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Transcript of Burn image classification using support vector machine
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
Agenda
Introduction 1
Burn Image Classification 2
Applying SVM for Burn Image Classification 3
Conclusion and Future Work 4
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IMAGE CLASSIFICATION INTRODUCTION
Image Classification
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Class 1
Class 2
…
Class L
Classifier
IMAGE CLASSIFICATION INTRODUCTION
Image classification process
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Input
Image
Pre-
processing
Feature
Extraction Classifier
Result
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)
IMAGE CLASSIFICATION
Image classification system using SVM
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Image classification system using SVM
Burn Images
4 degrees of burn:
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* The Journal of the Ameriacan Medical Association (Nov. 2014),
http://jama.jamanetwork.com/
Burn Image Classification
Illustration for degrees burning images
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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
Burn Image Classification
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Multi classes SVM
Adaptive SVM classifier for burn images
{x|(wT.x)+b= -1}
{x|(wT.x)+b= +1}
{x|(wT.x)+b=0}
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{x|(wT.x)+b= -1}
{x|(wT.x)+b= +1}
{x|(wT.x)+b=0}
N/A
o
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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
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Some Training Images
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Some Testing Images
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Experimental Results on Cho Ray supplying Dataset
http://fit.hcmup.edu.vn/medical_image_project/
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
REFERENCES
1. Janet, M., Torpy, M.D: Burn Injuries, the Journal of the American Medical Association
(JAMA), Vol 302, No. 16, doi:10.1001/jama.302.16.1828 (2009)
2. Michał S.:Introduction to Medical Imaging, Biomedical Engineering, IFE, 2013
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)
5. Guerbai, Y., Youcef C., and Bilal H.: The effective use of the one-class SVM classifier for
Handwritten signature verification based on writer-independent parameters." Pattern
Recognition (2014)
6. Chebira, A., Kovačević, J.. Multiresolution techniques for the classification of bioimage and biometric datasets. In Optical
Engineering+ Applications (pp. 67010G-67010G). International Society for Optics and Photonics.(2007)
7. Tam, T. D., and Binh, N. T.: Efficient Pancreas Segmentation in Computed Tomography
Based on Region-Growing. Nature of Computation and Communication. Springer
International Publishing, 332-340 (2014)
8. Bao, P. T.: Fast multi-face detection using facial component based validation by fuzzy logic. Proceedings of the
International conference on Image Processing and Computer Vision (IPCV’06), Las Vergas, Nevada, USA (2006)
9. Thai, L. H., Hai, T. S., Thuy, N. T.: Image Classification using Support Vector Machine and
Artificial Neural Network. I.J. Information Technology and Computer Science, Vol. 5, pp.
32-38, DOI: 10.5815/ijitcs.2012.05.05 (2012)
10. Van, H. T., Tat, P. Q., Le, T. H. Palmprint verification using GridPCA for Gabor features. In Proceedings of the Second
Symposium on Information and Communication Technology (pp. 217-225). ACM (2011).
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Thank You
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