Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
KEYWORDS: Convolutional neural networks, Data modeling, Databases, Feature extraction, Medical imaging, Neural networks, Machine learning, Computed tomography, Image retrieval, Neurons, Picture Archiving and Communication System
Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval
performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by
researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging
problems in current CBMIR research, which is mainly due to the well-known “semantic gap” issue that exists between
low-level image pixels captured by machines and high-level semantic concepts perceived by human. Recent years have
witnessed some important advances of new techniques in machine learning. One important breakthrough technique is
known as “deep learning”. Unlike conventional machine learning methods that are often using “shallow” architectures,
deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple
stages of transformation and representation. This means that we do not need to spend enormous energy to extract features
manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to
improve the accuracy and speed of a CBIR in integrated RIS/PACS.
In medical imaging informatics, content-based image retrieval (CBIR) techniques are employed to aid radiologists in the retrieval of images with similar image contents. CBIR uses visual contents, normally called as image features, to search images from large scale image databases according to users’ requests in the form of a query image. However, most of current CBIR systems require a distance computation of image character feature vectors to perform query, and the distance
computations can be time consuming when the number of image character features grows large, and thus this limits the
usability of the systems. In this presentation, we propose a novel framework which uses a high dimensional database to index the image character features to improve the accuracy and retrieval speed of a CBIR in integrated RIS/PACS.