Synthesis of cervical tissue second harmonic generation images using Markov random field modeling.

This paper presents a statistical image modeling approach based on Markov random field to synthesize cervical tissue second harmonic generation (SHG) images. Binary images representing fiber and pore areas of the cervix tissue are first obtained from SHG images using an image processing pipeline consisting of noise removal, contrast enhancement and optimal thresholding. These binary images are modeled using a Markov random field whose parameters are estimated via the least squares method. The parameters are then used to synthesize fiber and pore areas of cervical tissue in the form of binary images. The effectiveness of the synthesis is demonstrated by reporting the classification outcome for two classes of cervical SHG images collected from mice at two different stages of normal pregnancy. The developed synthesis allows generation of realistic fiber and pore area binary images for cervical tissue studies.