Algerian Journal of Electrical Systems and Sustainability
Volume 1, Numéro 1, Pages 27-35
2025-07-30
Tissue Segmentation Based On Two-way Anova And Gram-schmidt Orthogonal Polynomial Approximation
Authors : Grainat Youcef .
Abstract
This paper presents a method for segmenting tissues in images, regardless of their type or the nature of the material—whether from magnetic resonance imaging (MRI) or other sources. The approach relies on two numerical techniques: orthogonal polynomial approximation and the two-way ANOVA (anova2) statistical method. The study involves two types of images: a normal (healthy) image without impurities or tumors, and a deformed image, either artificially modified or representing a brain with a tumor. By applying both methods, tissue structures can be distinguished and enhanced, providing researchers and medical professionals with clearer image interpretations in a shorter time. The results demonstrate that while the anova2 method offers higher accuracy for material images, the orthogonal polynomial approximation proves more effective and faster for medical images.
Keywords
Tissue segmentation;MRI; orthogonal polynomial approximation; Gram-Schmidt; two-way ANOVA.
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