Publikationsdatenbank

Segmentation-free Radon transform algorithm to detect orientation and size of tissue structures in multiphoton microscopy images (2025)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Brandt, Danja (WE 2)

Nikishina, Anastasiia A.

Bias, Anne

Günther, Robert

Hauser, Anja E.

Duda, Georg N.

Beckers, Ingeborg E.

Niesner, Raluca A. (WE 2)

Quelle

Journal of biomedical optics

Bandzählung: 30

Heftzählung: 8

Seiten: 086001

ISSN: 1083-3668

Sprache

Englisch

Verweise

URL (Volltext): https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-30/issue-08/086001/Segmentation-free-Radon-transform-algorithm-to-detect-orientation-and-size/10.1117/1.JBO.30.8.086001.full

DOI: 10.1117/1.JBO.30.8.086001

Pubmed: 40772268

Kontakt

Institut für Veterinär-Physiologie

Oertzenweg 19 b
14163 Berlin
+49 30 838 62600
physiologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

Understanding the structural organization of biological tissues is critical for studying their function and response to physiological and pathological conditions. In vivo imaging techniques, such as multiphoton microscopy, enable high-resolution visualization of tissue architecture. However, automated orientation analysis remains challenging due to imaging noise, complexity, and reliance on manual annotations, which are time-consuming and subjective.

We present a Radon transform-based algorithm for robust, annotation-free structural orientation analysis across multimodal imaging datasets, aiming to improve objectivity and efficiency without introducing preprocessing artifacts.

The algorithm employs a patch-based Radon transform approach to detect oriented structures in noisy images. By analyzing projection peaks in Radon space, it enhances small structures' visibility while minimizing noise and artifact influence. The method was evaluated using synthetic and in vivo datasets, comparing its performance with human annotations.

The algorithm achieved strong agreement with human annotations, with detection accuracy exceeding 88% across different imaging modalities. Variability among trained raters emphasized the benefits of an objective, mathematically driven approach.

The proposed method provides a robust and adaptable solution for structural orientation analysis in biological images. Its ability to quantify tissue component orientation without preprocessing artifacts makes it valuable for high-resolution, dynamic studies in tissue architecture and biomechanics.