We propose a novel strategy for multi-atlas-based image segmentation of the prostate on magnetic resonance (MR) images using an ellipsoidal shape prior constraint algorithm. An ellipsoidal shape prior constraint was incorporated into the process of multi-atlas based segmentation to restrict the regions of interest on the prostate images and avoid the interference by the surrounding tissues and organs in atlas selection. In the subsequent process of atlas fusion, the ellipsoidal shape prior constraint calibrated and compensated for the shape prior obtained by the registration technique to avoid incorrect segmentation caused by registration errors. Evaluation of this proposed method on prostate images from 50 subjects showed that this algorithm was effective and yielded a mean Dice similarity coefficients of 0.8812, suggesting its high accuracy and robustness to segment the prostate on MR images.
目的: 为了有效的利用图谱的先验信息和待分割图像的灰度信息，提出一种新的椭球先验约束下的前列腺MR图像多图谱分割算法。
方法: 将多图谱分割与椭球形状先验相结合，在多图谱分割过程中引入椭球先验知识，针对椭球先验约束下的前列腺感兴趣区域进行图谱选择，大大避免了前列腺周围组织与器官对图谱选择造成的干扰；其次，在图谱融合过程中加入椭球先验项进行约束，对通过配准技术引入的前列腺图谱形状先验进行校正和补偿，有效避免了由配准误差引起的错误分割的情况。
结果: 对50例前列腺MR图像进行分割实验，实验结果表明该算法对前列腺数据的分割精度均在80%以上，平均精度提高到了88.12%。
结论: 椭球先验约束的前列腺MR图像多图谱分割算法稳定有效，分割结果精确度高。