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

传统水平集算法对初始轮廓的位置选择具有随机性,且缺少对边缘信息的处理,因此无法实现对脑组织边缘的准确提取。为此,融合分区与Canny泛函的水平集算法首先融合分区的思想,结合各区域的形态信息完成初始轮廓位置选定,使初始轮廓包含较多脑组织区域,提高了脑提取效率。其次,在能量泛函中融合了Canny算子,在保留传统水平集算法处理灰度不均匀图像的优越性的同时提高了对猕猴脑边缘检测的准确率。实验结果表明,该算法实现了对猕猴脑的准确提取,准确度最高可达到86%。

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%.