Medical image segmentation is the most complex and important task in the field of medical image processing and analysis, as it is linked to disease diagnosis accuracy. However, due to the medical image's high complexity and noise, segmentation performance is limited. We propose a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network model architecture to address the aforementioned issues (QPFC-DA). It has mechanisms for channel and spatial edge attention, which guide the content and edge segmentation branches, respectively. The bi-directional long short-term memory network was added after the two content segmentation branches to better integrate multi-scale features and prevent the loss of important features. Furthermore, the fuzzy C-means algorithm guided by the quadratic polynomial can better distinguish the image's weak edge regions and has a degree of noise resistance, resulting in a membership matrix with less ambiguity and a more reliable segmentation result. We also conducted comparison and ablation experiments on three medical data sets. The experimental results show that this method is superior to several other well-known methods.

医学图像分割是医学图像处理和分析领域中最复杂、最重要的任务，因为它关系到疾病诊断的准确性。然而，由于医学图像的高复杂度和噪声，分割性能是有限的。我们提出了一种新的二次多项式引导模糊 C 均值和双重注意机制复合网络模型架构来解决上述问题（QPFC-DA）。它具有通道和空间边缘注意机制，分别指导内容和边缘分割分支。在两个内容分割分支之后加入了双向长短期记忆网络，以更好地整合多尺度特征，防止重要特征丢失。此外，以二次多项式为指导的模糊C-means算法可以更好地区分图像的弱边缘区域，具有一定的抗噪能力，得到的隶属矩阵模糊度更小，分割结果更可靠。我们还对三个医学数据集进行了比较和消融实验。实验结果表明，该方法优于其他几种众所周知的方法。