An Intuitionistic Fuzzy Logic Driven UNet architecture: Application to Brain Image segmentation
This work addresses uncertainty in brain image segmentation for medical diagnosis and analysis, but it is incremental as it builds on the widely used UNet architecture.
The paper tackles the problem of uncertainty in MRI brain image segmentation due to partial volume effects by proposing IF-UNet, an enhanced UNet architecture that incorporates intuitionistic fuzzy logic to handle tissue ambiguity and boundary uncertainties. It reports improved segmentation quality on the IBSR dataset, with performance measured using accuracy, Dice coefficient, and IoU, though no specific numerical gains are provided.
Accurate segmentation of MRI brain images is essential for image analysis, diagnosis of neuro-logical disorders and medical image computing. In the deep learning approach, the convolutional neural networks (CNNs), especially UNet, are widely applied in medical image segmentation. However, it is difficult to deal with uncertainty due to the partial volume effect in brain images. To overcome this limitation, we propose an enhanced framework, named UNet with intuitionistic fuzzy logic (IF-UNet), which incorporates intuitionistic fuzzy logic into UNet. The model processes input data in terms of membership, nonmembership, and hesitation degrees, allowing it to better address tissue ambiguity resulting from partial volume effects and boundary uncertainties. The proposed architecture is evaluated on the Internet Brain Segmentation Repository (IBSR) dataset, and its performance is computed using accuracy, Dice coefficient, and intersection over union (IoU). Experimental results confirm that IF-UNet improves segmentation quality with handling uncertainty in brain images.