Researchers at UCI have developed a method of identifying, quantifying, and visualizing tissue with calcification. The image processing tool can automatically characterize calcium deposits in CT images histological tissue, especially when it has accumulated in unusual places in the body.
·Automated image processing for identification of calcification
·Aid in diagnosis of calcification-related disorders or diseases
·Automated image processing tool
·Provides quantification and post-processing visualization capabilities
·Has potential to be easily integrated into other image processing platforms
Currently, medical imaging modalities such as computerized tomography (CT) can be used by radiologists to initially identify calcium deposits in tissue of patients. These deposits of calcium can lead to tissue calcification in areas such as heart valves, arteries, and soft tissue leading to potentially dangerous effects if not identified.Doctors and scientists can also preserve tissue extracted from the body (in vitro) through histological techniques for further analysis to identify cell type, biomarkers, and even mineral accumulation, such as, calcium. More specifically, tissuecan be stained for calcium deposits (e.g. Von Kossa, Alizarin red staining etc), imaged under the microscope and then assessed by pathologists or by scientists for research purposes. However, many of the steps in this process require manual assessment,customization, and limited quantification capabilities.
Researchers at UCI have developed a method capable of streamlining the process of analyzing histological or CT images for tissue calcification. While the upstream preparations remain the same, the downstream image processing becomes automated. Using an open source image stitching software such as Fiji software,panoramic images of the tissue cross sections can be created. The images canbe characterized and processed in another computer software program, such as Matlab. Here the innovative program can record characteristics of every panoramic image such as the Red, Green, and Blue (RGB) value of every pixel and its location. Based on these parameters, the program can determine the distinguishing pixel factors between calcified and uncalcified areas and calculate a calcification ratio based on the respective pixel numberA dual color image can also be created to highlight the calcified and uncalcified tissue and visualize the regions of interest. This automated technology is a valuable tool for scientists and doctors, and provides a more quantitative assessment of calcification in in cardiac tissue,in and around native and artificial heart valves over time, and before and after medical procedures (some of which are intended to reduce calcification).
Working prototype has been used to process stained pericardial tissue