We develop image segmentation algorithms and patient-specific image-based 3D reconstructions. Reconstruction allows us to gain insight into qualitative features of the object which cannot be deduced from a single plane of sight, such as volume and the object relative position to others in the scene.
3D reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective.
We develop automated and smart algorithms for geometric and morphological image analysis.
Providing accurate morphometric information requires extensive knowledge and computational skills. Therefore, a mean to easily and accurately calculate such information is necessary to assess morphometrics in clinical settings.
We develop image analysis algorithms, mathematical models, machine learning and deep learning algorithms to analyze dicom images in order to identify anomalies and patterns for medical diagnostics.
Machine learning, including DL, is a fast-moving research field that has great promise for future applications in imaging and therapy. It is evident that DL has already pervaded almost every aspect of medical image analysis. “Conventional” image analysis methods were never intended to replace radiologists but rather to serve as a second opinion.
We develop algorithms that are commonly used to solve medical image registration problems: the process of aligning two or more images. The goal of an image registration method is to find the optimal transformation that best aligns the structures of interest in the input images.
Image registration is a crucial step for image analysis in which valuable information is conveyed in more than one image; i.e., images acquired at different times, from distinct viewpoints or by different sensors can be complementary. Therefore, accurate integration (or fusion) of the useful information from two or more images is very important.