This thesis present research work conducted in the field of retina image analysis. More specifically, the work is directed at the application of texture analysis technique for the segmentation of common retinal landmark and for retina image classification. The main challenge in this research is in identifying the suitable texture measurement for retina images. In this research we proposed the used of texture measurement based on Binary Robust Independent Elementary Features (BRIEF). BRIEF measure texture by performing an intensity comparison in a local image patch, thus it is very fast to compute and tolerant to any monotonic increase or decrease of image intensities, which makes the descriptor invariant to illumination. The performance of BRIEF as texture measurement is first shown in an experiment involving texture classification and segmentation using common texture datasets. The result demonstrates good performance from BRIEF in this experiment. BRIEF is next used in two applications of retinal image analysis, namely optic disc segmentation and glaucoma classification. In the former, we proposed the used of pixel classification using BRIEF as textural features and circular template matching to segment the optic disc. In addition, an extension of BRIEF called Rotation Invariant BRIEF (OBRIEF) is later proposed to improve the segmentation result. For glaucoma classification, we described two approaches for glaucoma classification using BRIEF/OBRIEF features. The first is based on determination of cup to disc ratio (CDR) and the second is classification using image features i.e. BRIEF features. Overall, our preliminary results on using BRIEF as texture measurement for retinal image analysis are encouraging and demonstrate that it has the potential to be used in retina image analysis.