Precision medicine which refers to a new treatment and prevention method based on understanding of individual gene, environment and life style have emerged as a new healthcare paradigm to lead future medicine. With the rapid progress of the fourth industrial revolution technologies such as big data, artificial intelligence, and internet of things, it is drawing attention to the opportunities and challenges of precision medicine. There is currently no comprehensive overview of precision medicine of approaches to scientometric analysis, and this study aims to provide an overview of the research and development trends in precision medicine by bibliometric network analysis. Total 7,324 articles were retrieved and analyzed using some scientometric analysis tools such as KnowledgeMatrix Plus, Gephi and VOSviewer software. Particularly, each nation's research activities and their global relative positions, international research collaboration in this field have also been analyzed and identified by scientometric method through network and co-word analysis and visualization maps.

We present a region adaptive subband image coding scheme using the statistical properties of image subbands for various subband decompositions. Motivated by analytical results obtained when the input signal to the subband decomposition is a unit step function, we analyze the energy packing properties toward the lower frequency subbands, edges, and the dependency of energy distribution on the orientation of the edges, in subband decomposed images. Based on these investigations and ideal analysis/synthesis filtering done in the frequency domain, the region adaptive subband image coding scheme extracts suitably shaped regions in each subband and then uses adaptive entropy-constrained quantizers for different regions under the assumption of a generalized Gaussian distribution for the image subbands. We also address the problem of determining an optimal subband decomposition among all possible decompositions. Experimental results show that visual degradations in the reconstructed image are negligible at a bit rate of 1.0 b/pel and reasonable quality images are obtainable at rates as low as 0.25 b/pel.

We improve the performance of conventional motion compensated discrete cosine transform video coding. For motion compensation, we employ a two step algorithm in which the camera motion is compensated first and then the motion of moving objects is estimated. We use a feature matching algorithm for camera motion compensation. Motion compensated frame differences are divided into three regions called stationary background, moving objects, and newly emerging area. A region adaptive subband image coding scheme is used for spatial coding of these regions.

The statistical properties of image subbands are studied for various subband decompositions. The authors observe that the amount of energy in image subbands increases towards the lower frequency subbands and, more importantly, towards the image edges, simultaneously. They also find that the directionality of energy distribution is highly dependent on the orientation of the edges. Based on the energy distribution, they extract arbitrarily shaped regions in each subband and employ the entropy-constrained quantizers using the generalized Gaussian distribution for modeling the image subbands. They also address the problem of determining an optimal subband decomposition among all the possible decompositions. The resulting feature based subband coding scheme yields at least 1-dB better PSNR than previously proposed schemes for the "Lena" image.>