There are many kinds of corporeal ingredients in urinary sediment which must be identified to confirm the diagnosis of an abnormality. In this paper, we refine a method which integrates both Gabor filter and scattering transform for texture analysis in urinary sediment images. The proposed scheme is based on the conventional Gabor filter and the recently developed scattering transform. The Gabor filter bank has the ability to capture the filtering responses according to the scale and orientation of texture. Besides, the scattering transformation provides a distinctive property of robust description, which is invariant to rotations and stable to spatial deformation. The excellent representation of Gabor filter and scattering transform has been severally studied in recent work, yet they have not been used in urinary sediment images. In this work, we propose to use both Gabor filter and scattering transformation to extract the texture feature of urinary sediment images. Coupling with an efficient support vector machine (SVM) classifier, the proposed scheme tends to shown superiority as compared to other single descriptive alternatives in real urinary sediment experiments.