In practical applications, effectively segmenting cracks in large-scale computed tomography (CT) images holds significant importance for understanding the structural integrity of materials. However, classical methods and Machine Learning algorithms often incur high computational costs when dealing with the substantial size of input images. Hence, a robust algorithm is needed to pre-detect crack regions, enabling focused analysis and reducing computational overhead. The proposed approach addresses this challenge by offering a streamlined method for identifying crack regions in CT images with high probability. By efficiently identifying areas of interest, our algorithm allows for a more focused examination of potential anomalies within the material structure. Through comprehensive testing on both semi-synthetic and real 3D CT images, we validate the efficiency of our approach in enhancing crack segmentation while reducing computational resource requirements.

翻译：在实际应用中，有效分割大型计算机断层扫描（CT）图像中的裂缝对于理解材料结构完整性具有重要意义。然而，经典方法和机器学习算法在处理输入图像的大尺寸时往往计算成本较高。因此，需要一种鲁棒算法来预检测裂缝区域，以实现聚焦分析并降低计算开销。本文提出的方法通过提供一种简化途径，以高概率识别CT图像中的裂缝区域。通过高效定位感兴趣区域，本算法能够对材料结构内的潜在异常进行更集中的检测。通过在半合成和真实三维CT图像上的全面测试，我们验证了该方法在增强裂缝分割效果的同时降低计算资源需求的有效性。