@Article{iasc.2021.016989,
AUTHOR = {Hongxia Deng, Chunxiang Hu, Zihao Zhou, Jinxiu Guo, Zhenxuan Zhang, Haifang Li},
TITLE = {RMCA-LSA: A Method of Monkey Brain Extraction},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {29},
YEAR = {2021},
NUMBER = {2},
PAGES = {387--402},
URL = {http://www.techscience.com/iasc/v29n2/42939},
ISSN = {2326-005X},
ABSTRACT = {The traditional level set algorithm selects the position of the initial contour randomly and lacks the processing of edge information. Therefore, it cannot accurately extract the edge of the brain tissue. In order to solve this problem, this paper proposes a level set algorithm that fuses partition and Canny function. Firstly, the idea of partition is fused, and the initial contour position is selected by combining the morphological information of each region, so that the initial contour contains more brain tissue regions, and the efficiency of brain tissue extraction is improved. Secondly, the canny operator is fused in the energy functional, which improves the accuracy of edge detection of rhesus monkey brain tissue while retaining the advantage of the traditional level set algorithm in processing an uneven gray image. Experimental results show that the algorithm can accurately extract the brain tissue of rhesus monkeys with an accuracy of up to 86%.},
DOI = {10.32604/iasc.2021.016989}
}