Quadratic polynomial guided fuzzy C-means and dual attention mechanism for medical image segmentation - Cai, Weiwei; Zhai, Bo; Liu, Yun; Liu, Runmin; Ning, Xin (Displays, 2021)
•This paper proposes a novel quadratic polynomial guided fuzzy C-means and dual attention mechanism composite network that can better distinguish the weak edge region in an image, has a certain level of noise resistance, can obtain a membership matrix with less fuzziness, and can obtain more secure segmentation results.•Taking into account that the current model with a constant as the division center is a special case of a quadratic polynomial surface as the division center. Therefore, this paper proposes dividing the data point set by the algebraic distance from the data point to the segmentation center, which has higher segmentation accuracy.•This paper designs a novel spatial edge attention module, which is mainly used to extract the edge information of the feature map to prevent the loss of important information and improve the edge segmentation ability of the model.•This paper conducted experiments on three well-known medical datasets. The comparison and ablation experiment results proved the effectiveness and superiority of the QPFC-DA algorithm. In addition, we also developed an Android APP that can be used in industrial production environments. 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.