Two new techniques for the compression of 2D and 3 D medical images are proposed in the paper. Both algorithms are based on DCT transforms and the aim is to ensure a nearly lossless image transmission and storage with a compression rate able to reduce significantly the amount ofdata. The first technique, used for the storage of2D medical images (microscope, radiographic, X-ray images, ultrasonic images, computer tomograph sections) is based on the extraction of a 'region of interest' in the original image. In fact, in most situations, only a limited region of the medical image is interesting for diagnosis. For compression, a direct DCT is applied on the original image, preserving a different number of coefficients inside and outside the region of interest (must higher inside the region). The compression ratio obtained with this method depends on the size ofthe selected region and on the number ofDCT coefficients preserved, ranging from 4 to 20. The second technique is based on the correlation existing between contiguous axial sections obtained from the CT. The 3D volume data is compressed using an original axial section and the difference images between successive sections. The advantage of this method consists in reducing the number of DCT coefficients necessary for a nearly lossless compression ofthe difference images. The compression ratio obtained ranges between 6 and 10, without significant losses in the image quality.
Abstract. This article proposes a solution to automatic color correction between two images/videos based on region correspondences. It starts with image segmentation by marker-controlled watershed transformation, which is faster and produces more uniform regions with better adherence to object boundaries than the segmentation in previous color correction approaches. Then, regions between two images are matched using point feature correspondences which are invariant to geometric transformation and illumination change. Finally, the color distorted image is corrected using the color statistics of corresponding regions and the color transfer functions weighted by influence masks. We demonstrate the experimental results using several data sets and evaluate the color correction by different measures of image similarity.
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