Time-space Fourier κω' filter for motion artifacts compensation during transcranial fluorescence brain imaging

Intravital imaging of brain vasculature through the intact cranium in vivo is based on the evolution of the fluorescence intensity and provides an ability to characterize various physiological processes in the natural context of cellular resolution. The involuntary motions of the examined subjects often limit in vivo non-invasive functional optical imaging. Conventional imaging diagnostic modalities encounter serious difficulties in correction of artificial motions, associated with fast high dynamics of the intensity values in the collected image sequences, when a common reference cannot be provided. In the current report, we introduce an alternative solution based on a time-space Fourier transform method so-called K-Omega. We demonstrate that the proposed approach is effective for image stabilization of fast dynamic image sequences and can be used autonomously without supervision and assignation of a reference image

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed