Improving Retrieval Performance in Medical Image Databases Using Simulated Annealing

In the area of content-based image retrieval, one of the prerequisites of successful retrieval is the extraction of an ample number of distinguishing features that sufficiently describe the important characteristics represented in the image content. Parameters underlying image segmentation and feature extraction need to be set appropriately in order to have this success in retrieval. We present here a parameter tuning method using simulated annealing to dynamically adjust values of important parameters used in customized image processing algorithms for the purpose of improving the performance of retrieval for high resolution CT lung images in computer-aided diagnosis. The most notable improvement using Fβ measure among five modules is from 0.56 to 0.81, which is a 44.64% increase (p=0.022). This method provides a way to improve retrieval performance in a large variety of applications in medical imaging informatics.