This chapter reviews most popular texture analysis approaches under novel comparison axes that are specific to biomedical imaging. A concise checklist is proposed as a user guide to assess the relevance of each approach for a particular medical or biological task in hand. We revealed that few approaches are regrouping most of the desirable properties for achieving optimal performance. In particular, moving frames texture representations based on learned steerable operators showed to enable data-specific and rigid-transformation-invariant characterization of local directional patterns, the latter being a fundamental property of biomedical textures. Potential limitations of having recourse to data augmentation and transfer learning for deep convolutional neural networks and dictionary learning approaches to palliate the lack of large annotated training collections in biomedical imaging are mentioned. We conclude by summarizing the strengths and limitations of current approaches, providing insights on key aspects required to build the next generation of biomedical texture analysis approaches.