Fuzzy inference system for detection CT and X-Ray image's edges

Medical imaging, including X-ray and Computed Tomography (CT) images, plays a crucial role in aiding doctors with the diagnosis and monitoring of diseases Highlighting significant details in these images can greatly assist doctors in making accurate diagnoses. This research proposes a novel and rapid technique based on a fuzzy inference system to extract details represented by the edges of the images. The proposed technique involves three steps: fuzzification, applying fuzzy rules, and defuzzification. It was tested on 60 image samples, consisting of 30 X-ray images and 30 CT images of the lung area. The results obtained from processing the CT images and X-ray images were compared, along with a comparative analysis between the two types of images. The proposed method showed a difference of 31.38% for dental X-ray images and 43.6% for CT images when compared to the traditional method (Sobel edge detection). This assessment was based on the quantitative evaluation of the results using the F-scale. Due to the difference in texture patterns between X-ray and CT images, there was a slight variation in the evaluations, approximately 10%.