Abstract
We propose a supervised learning approach for detecting landmarks in cardiac images from different views. A set of candidate landmark points are obtained using morphological operations and graph cut segmentation. The final landmarks are determined using random forests (RF) classifiers which were trained on low level features derived from the neighborhood of annotated landmarks on training images. We use features like intensity, texture, shape asymmetry and context information for landmark detection. Experimental results on the STACOM LV landmark detection challenge dataset show that our approaching is promising with room for further improvement.
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Mahapatra, D. (2013). Landmark Detection in Cardiac MRI Using Learned Local Image Statistics. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2012. Lecture Notes in Computer Science, vol 7746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36961-2_14
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DOI: https://doi.org/10.1007/978-3-642-36961-2_14
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