Fuzzy Edge Detection for the Identification of Arachnoid Cysts in Brain Images

Arachnoid cysts, prevalent yet challenging to accurately delineate in neuroimaging, can cause symptoms including headaches, seizures, and neurological deficits. Edge detection methods have been employed to identify these cysts; however, they often suffer from limitations such as sensitivity to noise and an inability to detect weak edges. This article proposes a novel application using fuzzy edge detection methodology for identifying Arachnoid cysts in brain images, leveraging the robustness and efficiency of fuzzy logic to handle uncertainty and noise. A comparison is drawn between the fuzzy edge detection method and traditional methods i.e., Robert, Sobel, Laplacian, Prewitt, and Canny, hypothesizing superior results with the fuzzy approach. The proposed method employs a Fuzzy Control System (FCS), adaptive thresholding, and inversion of the binary edge map. Implemented in Python due to its adeptness in handling large complex datasets and extensive library support, the method demonstrates clearer and more defined edges, enhancing visualization and interpretation of the cysts. With its robustness against noise and variability in image quality and its adaptability to different imaging conditions and patient cases, this approach shows significant potential for improving the accuracy of arachnoid cyst detection in clinical practice.