Large Scale Image Feature Extraction from Medical Image Analysis

Research in big data is focused on deriving knowledge from multiple data formats using intelligent analytics techniques. Medical analytics is a typical example, which encompasses data in multiple formats available as text, images and the data in the databases. Performing large scale image data analysis for near real time results is a challenging task. The challenge here is to extract features without compromising on the performance. Medical images are derived from multiple devices and are analyzed by health professionals manually which is qualitative. Automated deriving of intelligence and guidance will make the disease diagnosis accurate and faster. Automated analytics will also help to predict the progress of the disease and treatment plans. Data processing on huge image corpus is both storage and compute intensive. This paper presents a comparative survey of the image feature extraction techniques using parallel and high performance computing against nonparallel ones over the medical images. Keywords— Big data, Medical Image analysis, Feature extraction.