CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance are hard to interpret.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

Purpose: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

A MAP-Based Approach for Hyperspectral Imagery Super-resolution.

In this study, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction (SRR) problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov Random Field (MRF) based energy minimization approach is proposed and proved that the solution is quadratic.

Quantization index modulation-based image watermarking using digital holography.

We propose a blind watermarking method where the watermark is a hologram itself. In the proposed approach, the quantized phase of the hologram is embedded into the wavelet-transformed host image using quantization index modulation. In the detection stage, wavelet transform of the watermarked image followed by a minimum distance decoder is used.