Guest Editorial for Special Section on Multimodal Biomedical Imaging: Algorithms and Applications

J OINTLY assessing data from multiple modalities is inherent to many problems in science and engineering, but it is especially relevant to the analysis of biomedical imaging data. Technologies for imaging are individually expensive; thus, ways to synergistically derive information from complementary modalities are clearly attractive. Moreover, for instance, since each brain imaging modality is an indirect reflection of underlying neural activity at a different spatiotemporal scale, the emergence of multimodal neuroimaging analyses provides an unprecedented opportunity for understanding normal brain function and the pathophysiology of many brain diseases. Due to the sheer volume and increasing complexity of multimodal biomedical imaging data, there is critical need for new, advanced multimedia signal processing, modeling and computational methods. Such methods need to be fast, accurate and cost-effective means to probe underlying biological processes for novel imaging modalities to be adopted in real-world applications. Traditionally, biomedical imaging data has been analyzed from the approach of signal or image processing, but these submissions likely were published in journals outside the IEEE TRANSACTIONS ON MULTIMEDIA (TMM). With this special section, we would like to encourage future biomedical imaging submissions to TMM, and alert the multimedia community of the potential of multimodal biomedical imaging—an area of growing significance. The goal of this special section has been to bring together the imaging and media analysis communities to provide a diverse, but complementary, set of contributions to demonstrate the importance of multimedia signal processing and machine learning approaches in the multimodal biomedical imaging area. Through an open call for papers, we received 24 submissions, and after two rounds of professional reviews, nine papers were accepted for final publication. The final papers represent the state-of-art of this important and rapidly developing interdisciplinary area. This issue of TMM will provide a broad view of the recent advances in the wide field of multimodal biomedical imaging with respect to concepts, data acquisition, data analysis and fusion, and medical validation and direct clinical application. The first three papers are invited review papers. Multimodality optical imaging is one fast-emerging exciting area, especially when combined with other modalities, such as ultrasound. Such photoacoustic tomography (PAT) can overcome the fundamental penetration depth problem of light yet still achieve high-resolution optical imaging in deep tissues. The paper by Jeon and Kim, “Multimodal Photoacoustic