Image Processing Algorithms for Diagnostic Analysis of Microcirculation

• Main Author: Demir, Sumeyra Ummuhan
• Format: Others
• Published: VCU Scholars Compass 2010
• Subjects: microcirculation; segmentation; vessel detection; functional capillary density; Engineering
• Online Access: http://scholarscompass.vcu.edu/etd/137; http://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1136&context=etd

Microcirculation has become a key factor for the study and assessment of tissue perfusion and oxygenation. Detection and assessment of the microvasculature using videomicroscopy from the oral mucosa provides a metric on the density of blood vessels in each single frame. Information pertaining to the density of these microvessels within a field of view can be used to quantitatively monitor and assess the changes occurring in tissue oxygenation and perfusion over time. Automated analysis of this information can be used for real-time diagnostic and therapeutic planning of a number of clinical applications including resuscitation. The objective of this study is to design an automated image processing system to segment microvessels, estimate the density of blood vessels in video recordings, and identify the distribution of blood flow. The proposed algorithm consists of two main stages: video processing and image segmentation. The first step of video processing is stabilization. In the video stabilization step, block matching is applied to the video frames. Similarity is measured by cross-correlation coefficients. The main technique used in the segmentation step is multi-thresholding and pixel verification based on calculated geometric and contrast parameters. Segmentation results and differences of video frames are then used to identify the capillaries with blood flow. After categorizing blood vessels as active or passive, according to the amount of blood flow, quantitative measures identifying microcirculation are calculated. The algorithm is applied to the videos obtained using Microscan Side-stream Dark Field (SDF) imaging technique captured from healthy and critically ill humans/animals. Segmentation results were compared and validated using a blind detailed inspection by experts who used a commercial semi-automated image analysis software program, AVA (Automated Vascular Analysis). The algorithm was found to extract approximately 97% of functionally active capillaries and blood vessels in every frame. The aim of this study is to eliminate the human interaction, increase accuracy and reduce the computation time. The proposed method is an entirely automated process that can perform stabilization, pre-processing, segmentation, and microvessel identification without human intervention. The method may allow for assessment of microcirculatory abnormalities occurring in critically ill and injured patients including close to real-time determination of the adequacy of resuscitation.