Regions-of-interest-driven medical image compression

Advances in medical imaging technologies, particularly magnetic resonance imaging and multi-detector Computed Tomography (CT), have resulted in substantial increase in the size of datasets. In order to reduce the cost of storage and diagnostic analysis and transmission time without significant reduction in image quality, a state of the art image compression technique is required. We propose here a context based and regions of interest (ROI) based approach for the compression of 3D CT images and in particular vascular images, where a high spatial resolution and contrast sensitivity is required in specific areas. The methodology is developed based on the JPEG2000 standard to provide a variable level of compression in the (x,y) plane as well as in the z axis. The proposed lossy-to-lossless method compresses multiple ROIs depending on the degrees of clinical interest. High priority areas are assigned a higher precision (up to lossless compression) than other areas such as background. ROIs are annotated automatically. The method has been optimized and applied to the vascular images from CT angiography for peripheral arteries and compared with a standard medical image codec on 10 datasets regarding image quality and diagnostic performances. The average size of the compressed images can be reduced to 61, 60, 66, and 89 percent with respect to the lossless JP2K, Lossless JP3D, Lossless H.264, and original image respectively with no remarkable impairment for the diagnostic accuracy based on visual judgement of two radiologists.