Statistical geometric features-extensions for cytological texture analysis

Statistical geometric features (SGF) have recently been proposed for the classification of image textures. The SGF method is easily extended to use other geometric properties of connected regions. Following a brief review of the method, we propose such an extension to the set of SCF features for the purpose of classifying cervical cell textures. The resulting method proves to be as powerful as the gray level co-occurrence matrix (GLCM) method of texture analysis, when tested on a set of 117 cervical cell images. The ability to define features tailored to the geometric properties of the textures concerned makes this method a powerful analysis tool