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Open AccessArticle

Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing

Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibañez, Av. Diagonal Las Torres 2700, Santiago 7941169, Chile
Laboratorio de Virología Molecular, Fundación Ciencia & Vida, Av. Zañartu 1482, Santiago 7780272, Chile
Facultad de Medicina y Ciencia, Universidad San Sebastián, Lota 2465, Santiago 7510157, Chile
Author to whom correspondence should be addressed.
Biomolecules 2019, 9(12), 809;
Received: 1 November 2019 / Revised: 22 November 2019 / Accepted: 27 November 2019 / Published: 30 November 2019
Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona–Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique. View Full-Text
Keywords: cryo-EM; automatic particle selection; single particle picking cryo-EM; automatic particle selection; single particle picking
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Carrasco, M.; Toledo, P.; Tischler, N.D. Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing. Biomolecules 2019, 9, 809.

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