LAUSR

BACKGROUND AND OBJECTIVE In the state-of-the-art image transmission methods, multiple large medical images are usually transmitted one by one which is very inefficient. The objective of our study is to devise an effective and efficient multiple transmission optimization scheme for medical images called Mto via analyzing the visual content of the multiple images based on the characteristics of a recourse-constraint mobile telemedicine system (MTS) and the medical images; METHODS: To better facilitate the efficient Mto processing, two enabling techniques, i.e., 1) NIB grouping scheme, and 2) adaptive RIB replicas selection are developed. Given a set of transmission images (Ω), the correlation of these transmission images is first explored, the pixel resolutions of the corresponding MIBs keep high, the NIBs are grouped into k clusters based on the visual similarity in which the k RIBs are obtained. An optimal pixel resolution for the RIBs is derived based on the current network bandwidth and their corresponding areas, etc. Then, the candidate MIBs and the k RIBs are transmitted to the receiver node based on their transmission priorities. Finally, the IBs are reconstructed and displayed at the receiver node level for different users. RESULTS The experimental results show that our approach is about 45% more efficient than the state-of-the-art methods, significantly minimizing the response time by decreasing the network communication cost while improving the transmission throughput; CONCLUSIONS: Our proposed Mto method can be seamlessly applied in a recourse-constraint MTS environment in which the high transmission efficiency and the acceptable image quality can be guaranteed.