Fiji-Kappa

Fiji-Kappa measures curvature in biological image data using B-splines to quantify morphological features of tissues, cells, and sub-cellular structures.

Key Features:

• B-spline curvature representation: Uses B-splines to represent and measure curvature along continuous curves fitted to image structures.
• Manual initialization: Accepts a manually defined initial curve to guide subsequent computational fitting.
• Iterative minimization fitting: Employs an iterative minimization algorithm to fit the B-spline curve to underlying image data for refined curvature estimation.
• Synthetic-data validation: Validated using synthetic images generated from sine waves and golden spirals digitized across varying signal-to-noise ratios (SNR), pixel sizes, and point-spread functions (PSF), showing high correlation between measured and theoretical curvatures.
• Performance optimization: Fitting algorithms are optimized for higher SNR, smaller pixel sizes, and PSFs similar to those encountered in super-resolution microscopy, improving accuracy under those conditions.

Scientific Applications:

• Cytoskeletal filament analysis: Quantifies curvature of cytoskeletal filaments in vitro for studies of filament morphology and mechanics.
• Budding yeast cell wall analysis: Measures curvature of the budding yeast cell wall to assess morphological features of cell shape.
• Whole-worm locomotion analysis: Quantifies curvature of whole worms moving in an agar dish to study body posture and locomotion dynamics.
• Morphological and structural studies: Supports analysis of morphological changes, structural integrity, and dynamic processes in cellular and developmental biology.

Methodology:

Computational steps include manual definition of an initial curve, representation of the curve with B-splines, and iterative minimization to fit the B-spline curve to image data; validation employed synthetic images generated from sine waves and golden spirals digitized at varying SNR, pixel sizes, and PSF.