Conventional feature detection algorithms are largely based on clustered two-dimensional (2D) blocks of information. However, corners located at the centre of gradually greying blocks of information cannot be extracted using these algorithms. The edge feature points described by the algorithms are often affected by background changes, leading to significant differences in the descriptors for the same feature. These issues are detrimental to subsequent matching processes. Therefore, we propose a new feature detection method that will provide more useful corner information for subsequent tracking and detection processes, particularly for edge features. The edge information of corners is used to search for points that satisfy the requirements for inner greyscale consistency. The points are then used to construct point-symmetric structures. The zeroth-order inner greyscale data, first-order gradient orientation differences, and angular directions of the point-symmetric connections of the structures are considered structural attributes, which help search for feature points. Similar feature points are then clustered using a hierarchical clustering algorithm, followed by extracting the feature points from point pair features of the same type. It was experimentally demonstrated that the proposed point-symmetric structural features would help increase the number of valid feature points that can be extracted from an image.