One of the most significant recent advances in health information systems has been the shift from paper to electronic documents. While research on automatic text and image processing has taken separate paths, there is a growing need for joint efforts, particularly for electronic health records and biomedical literature databases. This work aims at comparing text-based versus image-based access ...

Medical imaging protocols produce large amounts of multimodal volumetric images. The large size of the datasets contributes to the success of supervised discriminative methods for semantic image segmentation. Classifying relevant structures in medical images is challenging due to (a) the large size of data volumes, and (b) the severe class overlap in the feature space. Subsampling the training ...

in this paper, an optimal algorithm is presented for de-noising of medical images. the presented algorithm is based on improved version of local pixels grouping and principal component analysis. in local pixels grouping algorithm, blocks matching based on l2 norm method is utilized, which leads to matching performance improvement. to evaluate the performance of our proposed algorithm, peak sign...

Quantitative Analysis and Visualization of PET Images (QAV-PET) is an opensource software implemented in the popular MATLAB coding environment that allows easy, intuitive, and efficient visualization and quantification of multimodal medical images. In particular, the software is well suited for PET-CT as well as MRI-PET images. It allows multi-modal images to be viewed simultaneously which allo...

consider the problem of joint enhancement of multichannel images with pixel based constraints on the multichannel data. We formulate an optimization problem that jointly enhances complex-valued mul-tichannel images while preserving the cross-channel information, which we include as constraints tying the multichannel images together. We first reformulate it as an equivalent (un-constrained) dual...

Medical imaging is the technique and process used to create images of the human body for clinical purposes seeking to reveal, diagnose medical science. It is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. The development of multimodality methodology based on nuclear medicine (NM), positron emission tomography (PET) imagin...

-Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledg...

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge...

Multimodal medical images are often of too different a nature to be registered on the basis of the image grey values only. It is the purpose of this chapter to construct operators that extract similar structures from these images that will enable registration by simple grey value based methods, such as optimization of cross-correlation. These operators can be constructed using only basic morpho...