A guide to building image-centric databases.
There is a paucity of image-centric neuroinformatics infrastructure within the individual investigator's laboratory despite the obvious need for automation and integration of experimental results. Yet, solutions can often be readily built using off-the-shelf databases and associated tools. Doing so simplifies day-to-day research operation and increases throughput. Proper construction of in-house solutions may also expedite community-wide integration of private and public data repositories. Here we describe neuroinformatics approaches at different levels of functionality, required expertise, and size of image datasets. The simplest approach offers ease of image browsing and rudimentary searching. More sophisticated systems provide powerful search capabilities, a means of tracking analysis, and even automated serial processing pipelines. In this practicum, we provide guidance in selecting among the different options.
Bug
William
W
Department of Neurobiology & Anatomy, Drexel College of Medicine, Philadelphia, PA 19129, USA.
Nissanov
Jonathan
J
D000818:Animals; D019295:Computational Biology; D003195:Computer Communication Networks; D016208:Databases, Factual / Q000191:economics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019451:Information Management / Q000191:economics; D016247:Information Storage and Retrieval / Q000191:economics; D008490:Medical Informatics / Q000191:economics / Q000379:methods; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009488:Neurosciences; D012984:Software; D018511:Systems Integration; D014055:Tomography, Emission-Computed; D014584:User-Computer Interface