Unsupervised Medical Image Segmentation Based on the Local Center of Mass

@article{Aganj2018UnsupervisedMI,
  title={Unsupervised Medical Image Segmentation Based on the Local Center of Mass},
  author={Iman Aganj and Mukesh G. Harisinghani and Ralph Weissleder and Bruce R. Fischl},
  journal={Scientific Reports},
  year={2018},
  volume={8}
}
Image segmentation is a critical step in numerous medical imaging studies, which can be facilitated by automatic computational techniques. Supervised methods, although highly effective, require large training datasets of manually labeled images that are labor-intensive to produce. Unsupervised methods, on the contrary, can be used in the absence of training data to segment new images. We introduce a new approach to unsupervised image segmentation that is based on the computation of the local… 
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References

SHOWING 1-10 OF 15 REFERENCES
Medical Image Segmentation: A Brief Survey
TLDR
Most popular medical image segmentation techniques are overviews and their capabilities, and basic advantages and limitations are discussed.
Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume II
TLDR
Most popular medical image segmentation techniques are overviews and their capabilities, and basic advantages and limitations are discussed.
Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology
Medical imaging is generally recognised as key to the success of better diagnosis and patient care and this book, describing state-of-the-art techniques, is a valuable contribution to the literature.…
A review of atlas-based segmentation for magnetic resonance brain images
Hybrid image segmentation using watersheds and fast region merging
TLDR
A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds and additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced.
A survey on deep learning in medical image analysis
Interactive Whole-Heart Segmentation in Congenital Heart Disease
TLDR
This work presents an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease, and shows that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short- axis slices.
Normalized Cuts and Image Segmentation
TLDR
This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Normalized cuts and image segmentation
  • Jianbo Shi, J. Malik
  • Computer Science
    Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1997
TLDR
This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TLDR
A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
...
1
2
...