融合分区与Canny泛函的水平集对猴脑提取的研究

Macaque brain extraction based on level set of fusion partition and Canny functional

The traditional level set has randomness in the location selection of the initial contour, and lacks the processing of edge information. Therefore, accurate extraction of brain tissue edges cannot be achieved. Therefore, firstly, the level set algorithm of fusion partition and Canny functional fuses the idea of partition and combines the morphological information of each region to complete the initial contour position selection, so that the initial contour contains more brain tissue, and improve the efficiency of brain tissue extraction. Secondly, the Canny operator is integrated into the energy functional, which improves the accuracy of detecting the edge of the macaque brain tissue while retaining the superiority of the traditional level set on the uneven grayscale image. Results show that the algorithm achieves accurate extraction of macaque brain tissue with an accuracy of up to 86%.