Sohei Yamakawa
ABSTRACT
With the development of machine learning techniques in the image processing field, research related to semantic segmentation has attracted much attention. Especially in medical image segmentation requires highly pixel-by-pixel accurate results. Given that it is difficult to obtain test images with their ground truth in the medical field, we aim to develop a robust method in an environment with few test images. Specifically, we improve Textonboost by using differential evolution and stochastic hill climbing methods. Experimental results showed that the proposed method outperformed conventional methods in terms of accuracy.
- Janez Brest, Ales Zamuda, Borko Boskovic, Mirjam Sepesy Maucec, and Viljem Zumer. 2009. Dynamic optimization using Self-Adaptive Differential Evolution. In 2009 IEEE Congress on Evolutionary Computation. 415--422. Google Scholar
Cross Ref
- Keiko Ono, Daisuke Tawara, and Yoshiko Hanada. 2019. Textonboost based on differential evolution. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 322--323. Google ScholarDigital Library
- Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi. 2009. Texton-boost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International journal of computer vision 81, 1 (2009), 2--23. Google ScholarDigital Library
Index Terms
- Textonmap optimization for spine segmentation using adaptive differential evolution
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