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3 November 2020 2D PCA for automatic segmentation of the prostate in ultrasound images
F. Arámbula Cosío, Zian Fanti, F. Torres Robles
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Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830E (2020) https://doi.org/10.1117/12.2576149
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru
Abstract
Accurate automatic segmentation of the prostate in ultrasound images is still a challenging research problem. In this work, we propose the use of gray level images, constructed with a sample of gray level profiles perpendicular to the contour of the prostate. A two dimensional principal component analysis (2D PCA) was performed on a set of training contour images. The reconstruction error from the 2D PCA was used as an objective function for automatic adjustment of a point distribution model of the prostate. Our method was validated on 9 ultrasound images of the prostate and compared to the optimization of an objective function based on the mean Mahalanobis distance of a sampled gray level profile to the corresponding statistical profile model. Our new method based on a 2D PCA shows improved prostate segmentation results.
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F. Arámbula Cosío, Zian Fanti, and F. Torres Robles "2D PCA for automatic segmentation of the prostate in ultrasound images", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830E (3 November 2020); https://doi.org/10.1117/12.2576149
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KEYWORDS
Image segmentation
Prostate
Principal component analysis
Ultrasonography
Mahalanobis distance
Optimization (mathematics)
Statistical modeling
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