In bio-medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing. Data from many different sources can be collected into massive data sets via localized sensor networks. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. The application of bio-medical image fusion for big-data computing has strong development momentum, big-data bio-medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing. The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big-data bio-medical image of WSN is proposed in this paper. In this method, the three high-frequency coefficients in wavelet domain of bio-medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data bio-medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on multi-scale edge of bio-medical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of big-data bio-medical image, which can solve the problem of data redundancy, more energy cost and low quality
