11 March 2011 Automated vasculature extraction from placenta images
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79621L (2011) https://doi.org/10.1117/12.878343
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Recent research in perinatal pathology argues that analyzing properties of the placenta may reveal important information on how certain diseases progress. One important property is the structure of the placental blood vessels, which supply a fetus with all of its oxygen and nutrition. An essential step in the analysis of the vascular network pattern is the extraction of the blood vessels, which has only been done manually through a costly and time-consuming process. There is no existing method to automatically detect placental blood vessels; in addition, the large variation in the shape, color, and texture of the placenta makes it difficult to apply standard edge-detection algorithms. We describe a method to automatically detect and extract blood vessels from a given image by using image processing techniques and neural networks. We evaluate several local features for every pixel, in addition to a novel modification to an existing road detector. Pixels belonging to blood vessel regions have recognizable responses; hence, we use an artificial neural network to identify the pattern of blood vessels. A set of images where blood vessels are manually highlighted is used to train the network. We then apply the neural network to recognize blood vessels in new images. The network is effective in capturing the most prominent vascular structures of the placenta.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nizar Almoussa, Nizar Almoussa, Brittany Dutra, Brittany Dutra, Bryce Lampe, Bryce Lampe, Pascal Getreuer, Pascal Getreuer, Todd Wittman, Todd Wittman, Carolyn Salafia, Carolyn Salafia, Luminita Vese, Luminita Vese, } "Automated vasculature extraction from placenta images", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79621L (11 March 2011); doi: 10.1117/12.878343; https://doi.org/10.1117/12.878343

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