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This paper presents an image processing system to distinguish a lipopolysaccharide (LPS) infection model of preterm labor from normal mouse pregnancy using Second Harmonic Generation (SHG) images of mouse cervix. Two classes of SHG images are considered: images from mice in which premature birth was caused by intrauterine LPS administration and images from normal pregnant mice. A wide collection of image texture features consisting of co-occurrence matrix-based, granulometry-based and wavelet-based are examined. The results obtained indicate that the combination of co-occurrence-based and granulometry-based textures features provides the most effective texture set for separating these two classes of images.