1,721,778 research outputs found
Welcome
No full textThe 9th International Symposium on Biomedical Imaging (ISBI'12) was held Apr 30 - May 5, 2012, at the Centre Convencions International Barcelona (CCIB), in Barcelona, Spain. This is the third occasion that the meeting is held in Europe. The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging and image computing. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). ISBI 2012 will be the 9th meeting in this series and its 10th anniversary since the first edition. Previous meetings have played an important role in facilitating interaction between medical and biological imaging researchers. The 2012 meeting will continue this tradition of fostering knowledge transfer between different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation. The 2012 meeting will be preceded by two days of Satellite Open Source Workshops organized in conjunction with the EuroBioimaging consortium on Apr 30th and May 1st. These workshops will gather members from the biological and medical imaging communities to understand their needs and share new ideas for future developments of open source software tools. Two workshops have been organized: Bioimage Analysis Workshop and Medical Image Analysis Workshop. We received 701 submissions for the traditional full-paper track and 93 abstract submissions for the new abstractonly track; 48 full-paper submissions were solicited for inclusion in the special sessions. To ensure high quality of all accepted papers, each full-paper paper was sent out to three to four reviewers, of which at least one was a member of the Bio Imaging and Signal Processing (BISP, from the SPS) or the Biomedical Imaging and Image Processing (BIIP, from the EMBS) committees, the two expert IEEE Technical Committees involved with ISBI. Based on the reports from the reviewers, the Program Chairs together with help from the Technical Program Committee selected 135 (19%) papers for contributed oral sessions, 295 (42%) for full-paper poster sessions and 76 (82%) abstract-only poster sessions. The chart below provides an overview on how the various categories of papers have evolved over the 10 years of ISBI history. The total number of attendees for 2012 is based on the date of submission of this Welcome letter more than a month before the conference
Mathematical preliminaries
No full textIn this chapter we review well-known mathematical tools that will be used throughout the book. Specifically, we review basic concepts on images and measures of their quality, vector and matrix results, linear processing in the image domain and its associated Fourier domain, calculus and variations, and we finish with some notions on shape analysis. Some results are analytically derived so that the reader can gain insight in some topics considered more relevant.</p
Preface to the Proceedings of Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015
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Geometry Regularized Joint Dictionary Learning for Cross-Modality Image Synthesis in Magnetic Resonance Imaging
No full textMulti-sequence MRI protocols are used in comprehensive examinations of various pathologies in both clinical diagnosis and medical research. Various MRI techniques provide complementary information about living tissue. However, a comprehensive examination covering all modalities is rarely achieved due to considerations of cost, patient comfort, and scanner time availability. This may lead to incomplete records owing to image artifacts or corrupted or lost data. In this paper, we explore the problem of synthesizing images for one MRI modality from an image of another MRI modality of the same subject using a novel geometry regularized joint dictionary learning framework for non-local patch reconstruction. Firstly, we learn a cross-modality joint dictionary from a multi-modality image database. Training image pairs are first co-registered. A cross-modality dictionary pair is then jointly learned by minimizing the cross-modality divergence via a Maximum Mean Discrepancy term in the objective function of the learning scheme. This guarantees that the distribution of both image modalities is taken jointly into account when building the resulting sparse representation. In addition, in order to preserve intrinsic geometrical structure of the synthesized image patches, we further introduced a graph Laplacian regularization term into the objective function. Finally, we present a patch-based non-local reconstruction scheme, providing further fidelity of the synthesized images. Experimental results demonstrate that our method achieves significant performance gains over previously published techniques
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
No full textSimulation and synthesis in medical imaging:Second international workshop, SASHIMI 2017 held in conjunction with MICCAI 2017 Québec city, QC, Canada, September 10, 2017 proceedings
No full textSimulation and synthesis in medical imaging:Second international workshop, SASHIMI 2017 held in conjunction with MICCAI 2017 Québec city, QC, Canada, September 10, 2017 proceedings
No full textRegion-Enhanced Joint Dictionary Learning for Cross-Modality Synthesis in Diffusion Tensor Imaging
No full textDiffusion tensor imaging (DTI) has notoriously long acquisition times, and the sensitivity of the tensor computation often make this technique vulnerable to various interferences, for example, physiological motions, limited scanning time and patients with different medical conditions. In neuroimaging, studies usually involve different modalities. We considered the problem of inferring key information in DTI from other modalities. To address such a problem, several cross-modality image synthesis approaches have been proposed recently, in which the content of an image modality is reproduced based on those of another modality. However, these methods typically focus on two modalities of same complexity. In this work we propose a region-enhanced joint dictionary learning method that combines the region-specific information in a joint learning manner. The proposed method encodes intrinsic differences among different modalities, while the jointly learned dictionaries preserve common structures among them. Experimental results show that our approach has desirable properties on cross-modality image synthesis in diffusion tensor images.</p
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