**5. Conclusion and future directions**

Fem1b functions in vivo to regulate insulin secretion and plasma glucose levels. Fem1b-KO mice do not have fasting hyperglycemia but rather have defective acutephase GSIS. Such defective acute-phase GSIS is the earliest detectable defect in humans destined to develop diabetes and may represent the primary genetic risk factor predisposing to diabetes [23]. With aging and superimposed insulin resistance, fasting hyperglycemia and overt diabetes later develop. Therefore, the Fem1b-KO mouse model is a key component of the complex pathogenesis of type 2 diabetes mellitus. Both male and female homozygotes display abnormal glucose tolerance. The role of Fem1b in pancreatic islet insulin secretion strengthens evidence that a genetic pathway homologous to nematode sex determination may be involved in mammalian glucose homeostasis. This novel pathway could be involved in the β-cell dysfunction seen in type 2 diabetes mellitus. Since Calpain-10/NIDDM1 is known to interact with a gene that is near where human FEM1B localizes, to increase susceptibility to type 2 diabetes[24], whether FEM1B could be the responsible interacting gene becomes a pertinent question Although the mechanism of this regulation by Fem1b remains to be established, this finding strengthens evidence that a genetic pathway homologous to nematode sex determination may be involved in mammalian glucose homeostasis and promises to offer insight into novel genes and processes as potential candidates in the pathogenesis of diabetes mellitus.

Multi-modality analysis has been emerging as a major trend in the remote sensing, computer visualization, and biomedical image fusion. Fusing biomedical images is a very challenging problem because of the possible vast content change and non-uniform distributed intensities of the involved images. The new algorithm presented in this chapter, which consists of the Adaptive Exploratory Algorithm for the control point detection and heuristic optimization fusion, is reliable and time efficient. The new approach has achieved an excellent result by giving the visualization of fundus image with a complete angiogram overlay. By locking the multi-sensor images in one place, the algorithm allows ophthalmologists to match the same eye over time to get a sense of disease progress and pinpoint surgical tools to increase accuracy and speed of the surgery. The new algorithm can be easily expanded to human or animals' 3D eye, brain, or body image feature extraction, registration, and fusion. Many biomedical registration and fusion methods are still primarily used for research activity [19]. Very few of them have been developed into the integrated user-friendly computer software. The eventual aim is to developing and distributing the advanced and easy-to-use software which is suitable for various clinical environments. This plan requires intensive user interface developing work, which allows users adjusting a few threshold parameters if necessary. The user interface must be stable, simple and informative for every day clinical routine, because most of the end-users do not have much knowledge about algorithms and computer programs. Working closely with clinicians is extremely important when applying the new methods to practical clinic applications. As computation speed has been dramatically increased, real-time live ophthalmic image processing [25] will be used to handle larger and larger volumes of data in short periods. The data transmission rate, image size, higher resolution pixels, and many other issues will inevitably stress such live imaging fusion systems. The algorithms presented in this book have potential ability to handle those challenges. The presented method is a promising step towards useful clinical tools for retinopathy diagnosis, and thus forms a good foundation for further development.
