Histologic imaging plays an important role in discriminating cancerous tissues of several body organs. However, the human histopathological examinations may be subjective and error prone, because of the complexity of the appearances of the histologic texture. These limitations can be overcome by adopting quantitative computational methods with human histopathological examination routines. This study proposes a new feature descriptor to characterize texture of histologic images. The proposed method derives a discriminative feature space by observing the self-similarity characteristics of the texture based on fractal geometry. The merit of utilizing fractal geometry to describe the histologic texture is assessed by a classification experiment. The experimental results indicate that the proposed feature descriptor can classify cancer and non-cancer tissues of histologic images of liver and prostate images around 95% of correct classification rate.