This paper presents an advanced region growing method for precise left atrium (LA) segmentation and estimation of atrial wall thickness in CT/MRI scans. The method leverages a Gaussian mixture model (GMM) and fractal dimension (FD) analysis in a three-step procedure to enhance segmentation accuracy. The first step employs GMM for seed initialization based on the probability distribution of image intensities. The second step utilizes fractal-based texture analysis to capture image self-similarity and texture complexity. An enhanced approach for generating 3D fractal maps is proposed, providing valuable texture information for region growing. In the last step, fractal-guided 3D region growing is applied for segmentation. This process expands seed points iteratively by adding neighboring voxels meeting specific similarity criteria. GMM estimations and fractal maps are used to restrict the region growing process, reducing the search space for global segmentation and enhancing computational efficiency. Experiments on a dataset of 10 CT scans with 3,947 images resulted in a Dice score of 0.85, demonstrating superiority over traditional techniques. In a dataset of 30 MRI scans with 3,600 images, the proposed method achieved a competitive Dice score of 0.89±0.02, comparable to Deep Learning-based models. These results highlight the effectiveness of our approach in accurately delineating the LA region across diverse imaging modalities.

中文翻译：

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FractalRG：使用高斯混合模型进行左心房分割的高级分形区域生长

本文提出了一种先进的区域生长方法，用于精确左心房 (LA) 分割和 CT/MRI 扫描中心房壁厚度的估计。该方法通过三步过程利用高斯混合模型 (GMM) 和分形维数 (FD) 分析来提高分割精度。第一步基于图像强度的概率分布采用 GMM 进行种子初始化。第二步利用基于分形的纹理分析来捕获图像的自相似性和纹理复杂性。提出了一种生成 3D 分形图的增强方法，为区域生长提供有价值的纹理信息。在最后一步中，应用分形引导的 3D 区域生长进行分割。该过程通过添加满足特定相似性标准的相邻体素来迭代地扩展种子点。GMM估计和分形图用于限制区域生长过程，减少全局分割的搜索空间并提高计算效率。在包含 10 次 CT 扫描和 3,947 张图像的数据集上进行的实验得出的 Dice 得分为 0.85，证明了相对于传统技术的优越性。在 30 次 MRI 扫描、3,600 张图像的数据集中，所提出的方法获得了 0.89±0.02 的有竞争力的 Dice 分数，与基于深度学习的模型相当。这些结果凸显了我们的方法在通过不同成像方式准确描绘洛杉矶区域方面的有效性。