Implementation Based on Hadoop Ophthalmic Imaging

With the rapid development of ophthalmic treatment sector, the realtime access to vast amounts of data of patient treatment and image information plays a particularly important role in the aspect of medical imaging storage and management. Nowadays, the cloud platform of distributed storage based on Hadoop is becoming more and more popular in the field of industry; but the Hadoop is for the storage of large files while the ophthalmic image and other medical images are generally of 500KB size, or even smaller. They are stored in the Hadoop, which results in its bottleneck. As a result, various small images merge to file into a sequence of operations as far as possible and stores the merged file into the DataNode, reducing the number of files stored in HDFS. And it is not immediate into the HDFS data nodes when the small file has to be transferred after combination. It is put into the cache server and used to wait for Serialization File, and finally uploaded to the HDFS so that a large amount of ophthalmic images can be effectively stored in the Hadoop platform.