In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a no...

In this paper we propose a two-step mutual informationbased algorithm for medical image segmentation. In the first step, the image is structured into homogeneous regions, by maximizing the mutual information gain of the channel going from the histogram bins to the regions of the partitioned image. In the second step, the intensity bins of the histogram are clustered by minimizing the mutual inf...

we propose a method for medical image denoising using calculus of variations and local variance estimation by shaped windows. this method reduces any additive noise and preserves small patterns and edges of images. a pyramid structure-texture decomposition of images is used to separate noise and texture components based on local variance measures. the experimental results show that the proposed...

The abundance of widely available digital data poses exciting new opportunities. Previously this data was mostly textual, today it includes text, speech, audio, images, video, and other representations. The challenge now is to organize, understand, and search this multimodal information in a robust, efficient and intelligent way, and to create dependable systems that allow natural and intuitive...

Rigorous statistical analysis of multimodal imaging datasets is challenging. Mass-univariate methods for extracting correlations between image voxels and outcome measurements are not ideal for multimodal datasets, as they do not account for interactions between the different modalities. The extremely high dimensionality of medical images necessitates dimensionality reduction, such as principal ...

This paper introduces a new free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information, seen from certain points and produces images, called surface signatures, at these points. Matching signatures of di...

In this paper, we introduce an image segmentation framework which applies automatic threshoding selection using fuzzy set theory and fuzzy density model. With the use of different types of fuzzy membership function, the proposed segmentation method in the framework is applicable for images of unimodal, bimodal and multimodal histograms. The advantages of the method are as follows: (1) the thres...

Optical flow models are widely used for different image registration applications due to their accuracy and fast computation. Major disadvantages to overcome for medical image registration are large deformations and inaccurate regularisation at discontinuities, which cannot be modelled accurately with quadratic regularisers, and an intensity dependent energy term, which does not allow for image...

This paper describes the joint participation of the TIA-LabTL (INAOE) and the MindLab research group (UNAL) at the ImageCLEF 2015 Scalable Concept Image Annotation challenge subtask 2: generation of textual descriptions of images noisy track. Our strategy relies on a multimodal representation that is built in an unsupervised way by using the associated text to images and the visual features tha...