Abstract
A novel gland segmentation and classification scheme applied to an H&E histology image of the prostate tissue is proposed. For gland segmentation, we associate appropriate nuclei objects with each lumen object to create a gland segment. We further extract 22 features to describe the structural information and contextual information for each segment. These features are used to classify a gland segment into one of the three classes: artifact, normal gland and cancer gland. On a dataset of 48 images at 5× magnification (which includes 525 artifacts, 931 normal glands and 1,375 cancer glands), we achieved the following classification accuracies: 93% for artifacts v. true glands; 79% for normal v. cancer glands, and 77% for discriminating all three classes. The proposed method outperforms state of the art methods in terms of segmentation and classification accuracies and computational efficiency.
www.cse.msu.edu/~nguye231/GlandSegClass.html
Description: The code for gland segmentation and feature extraction, the details of the experiments (PPMM-SF method, PPMM-SCF method, three methods to estimate p(y|x) mentioned in the paper)
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Keywords
- Contextual Information
- Segmentation Result
- Jaccard Index
- Median Absolute Deviation
- Prostate Cancer Detection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Nguyen, K., Sarkar, A., Jain, A.K. (2012). Structure and Context in Prostatic Gland Segmentation and Classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_15
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DOI: https://doi.org/10.1007/978-3-642-33415-3_15
Publisher Name: Springer, Berlin, Heidelberg
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