Abstract
This paper addresses the problem of automatic location of landmarks used for the analysis of MRI cardiac images. Typically the landmarks of shapes in MRI images are located manually which is a time consuming process requiring human expertise and attention to detail. As an alternative a number of researchers use shape modelling and image search techniques for locating the required landmarks automatically. Usually these techniques require human expertise for initializing the search and in addition they require high quality, noise free images so that the image-based landmark location is successful. With our work we propose the use of neural network methods for learning the geometry of sets of points so that it is possible to predict the positions of all required landmarks based on the positions of a small subset of the landmarks rather than using image-data during the process of landmark-location. As part of our work the performance of neural network methods like Multilayer Perceptrons, Radial Basis Functions and Support Vector Machines is evaluated. Quantitative and visual results demonstrate the potential of using such methods for locating the required landmarks on endo-cardial and epicardial landmarks of the left ventricle of MRI cardiac images.
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Jayne, C., Lanitis, A., Christodoulou, C. (2012). Automatic Landmark Location for Analysis of Cardiac MRI Images. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_21
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DOI: https://doi.org/10.1007/978-3-642-32909-8_21
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