원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.9 No.2
2016.02
pp.179-188
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, the existing microscopic image segmentation method is studied, by comparing the segmentation result, the support vector machine (SVM) of microscopic image segmentation method has good precision, deserves further research.
목차
1. Introduction
2. The Existing Cell Nucleus Extraction Method
2.1. The Method Based on Threshold
2.2. The Method based on Gradient (including Amplitude and Direction)
2.3. The Method based on Region
3. Support Vector Machine (SVM) Method
3.1. The Thoughts of Support Vector Machine (SVM) Applied to Split
3.2. Support Vector Machine Segmentation Experiments
3.3. Effects of Different Samples Number for the Accuracy of the Segmentation
4. The analysis of Result
4.1. Threshold Method
4.2. The Gradient Operator Method and K-Means Clustering Algorithm in the Lab Color Space
5. Conclusion
References
2. The Existing Cell Nucleus Extraction Method
2.1. The Method Based on Threshold
2.2. The Method based on Gradient (including Amplitude and Direction)
2.3. The Method based on Region
3. Support Vector Machine (SVM) Method
3.1. The Thoughts of Support Vector Machine (SVM) Applied to Split
3.2. Support Vector Machine Segmentation Experiments
3.3. Effects of Different Samples Number for the Accuracy of the Segmentation
4. The analysis of Result
4.1. Threshold Method
4.2. The Gradient Operator Method and K-Means Clustering Algorithm in the Lab Color Space
5. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보