This study evaluates structural variations in two Avicennia species to assess ecosystem dynamics and resilience to environmental changes using scale-space profiling. Since leaf thickness and vein patterns are unique to each Avicennia community, an appropriate filtering method is needed for accurate geometric pattern analysis, such as fractal dimension calculation, which depends on precise image boundary identification. Techniques such as vein isolation via Gaussian blur and adaptive thresholding, edge detection using Canny, image scaling, and the Laplacian of Gaussian (LoG) filter are employed. The LoG filter method demonstrates greater accuracy based on Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Figure of Merit (FoM), the last of which could not be determined due to a rare case of uncertainity. The results indicate that the LoG filter is a preferable choice for assessing structural patterns in Avicennia marina and Avicennia officinalis species. The study evaluates the time efficiency of different image-processing techniques, emphasizing edge detection and vein isolation. The Laplacian of Gaussian (LoG) filter, despite its computational complexity \(O(n+m)\) , provides superior accuracy. Additionally, the Gaussian pyramid technique is employed for fractal analysis in leaf structures following LoG filtering, aiming to enhance fractal analysis for studying leaf geometric patterns.