Abstract
Selective segmentation is an important aspect of image processing. Being able to reliably segment a particular object in an image has important applications particularly in medical imaging. Robust methods can aid clinicians with diagnosis, surgical planning, etc. Many selective segmentation algorithms use geometric constraints such as information from the edges in order to determine where an object lies. It is still a challenge where there is low contrast present between two objects, and an edge is difficult to detect. Relying on purely edge constraints in this case will fail. We aim to make use of area constraints in addition to edge information in a segmentation model which is robustly capable of segmenting regions in an image even in the presence of low contrast, when given suitable user input. In addition, we implement a deep learning algorithm based on this model, allowing for a supervised, semi-supervised or unsupervised approach, depending on data availability.
Work supported by UK EPSRC grant EP/N014499/1.
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Burrows, L., Chen, K., Torella, F. (2020). On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_8
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