Newest papers

Image Moment Estimation for Crowded Datasets

Andy Zhang, Oscar Mickelin, Amit Singer · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.22840 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Single-particle cryogenic electron microscopy (cryo-EM) adalah teknik pencitraan yang mampu merekonstruksi struktur makromolekul biologi tiga dimensi (3D) resolusi tinggi dari serangkaian gambar proyeksi berisik yang diambil di arah pencitraan acak. Model matematika untuk gambar cryo-EM biasanya mengasumsikan bahwa setiap gambar mengandung persis satu partikel, yang berpusat pada asal-usul. Namun, banyak set data eksperimental mengandung gambar partikel yang berisi beberapa partikel: satu partikel pusat dan beberapa partikel yang mengisi. Deviasi ini dari model menyebabkan perkiraan statistik gambar yang tidak akurat, seperti momen gambar berurutan. Untuk mempertimbangkan ini, kami mengusulkan metode untuk memperkirakan momen gambar sambil memperbaiki efek partikel mengisi. Metode ini dapat dijalankan langsung dari serangkaian micrographs cryo-EM

Machine translation (Meta M2M100); the English original follows.

Abstract

Single-particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of reconstructing high-resolution three-dimensional (3D) structure of biological macromolecules from a set of noisy projection images taken at random viewing directions. The mathematical model for cryo-EM images typically assumes that each image contains exactly one particle, which is centered at the origin. However, many experimental datasets contain particle images which include multiple particles: one central particle and a few crowding particles. This deviation from the model leads to inaccurate estimation of image statistics, such as the low-order image moments. To account for this, we propose a method to estimate image moments while correcting for the effect of crowding particles. This method can be run directly from a set of cryo-EM micrographs with picked particles, and we demonstrate its efficacy on a wide array of synthetic data.

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Topics: Image (mathematics), Artificial intelligence, Moment (physics), Set (abstract data type), Computer vision, Projection (relational algebra)

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