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Automated Lung Cancer Detection From PET/CT Images Using Texture and Fractal Descriptors
Published in Ayman El-Baz, Jasjit S. Suri, Lung Imaging and CADx, 2019
K. Punithavathy, Sumathi Poobal, M. M. Ramya
Highly complex, irregular fractal structures exhibit greater FD values [44]. Fractal objects having various textures and spatial arrangements may result in the same FD value because of the combined differences in roughness, coarseness, and directionality. Mandelbrot and Pignoni [67] introduced lacunarity as a complementary parameter to differentiate fractal objects yielding similar FD. Lacunarity analyzes the distribution of holes in an object to measure the amount of nonuniformity in an image. Lacunarity [44] is computed by Lacunarity=(Variance/Mean2)−1
Two image quality assessment methods based on evidential modeling and uncertainty: application to automatic iris identification systems
Published in International Journal of Computers and Applications, 2023
Amina Kchaou, Sonda Ammar Bouhamed
Calculation of lacunarity: Lacunarity corresponds generally to the visual impressions of textual uniformity in images. Its measure is defined by Equation (10): where σ is the standard deviation and μ is the mean of the pixels per box (Box) at . Figure 3 represents the effect of the fractal pattern on the lacunarity measure. The smaller the lacunarity measure, the more irregular (non-fractal) the pattern is.
A Chaotic Approach to Recognize the Characteristics of Genetic Codes of Covid Patients
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Lacunarity (Lac) is a scale-dependent metric of heterogeneity, texture, translational and rotational invariance of an object. It is essentially a pixel distribution of a picture that may be derived from different box sizes () at different grid directions ().