Morphometric analysis of brain images with reduced number of statistical tests: A study on the gender-related differentiation of the corpus callosum

• Published in: Artificial intelligence in medicine Vol. 47; no. 1; pp. 75 - 86
• Main Authors: Kontos, Despina, Megalooikonomou, Vasileios, Gee, James C
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-09-2009
• Subjects: Algorithms; Brain - anatomy & histology; Corpus callosum; Corpus Callosum - anatomy & histology; Databases, Factual; Dynamic recursive partitioning; Feature extraction; Female; Humans; Image analysis; Image Processing, Computer-Assisted - methods; Internal Medicine; Magnetic Resonance Imaging; Male; Models, Statistical; Multiple comparison problem; Other; Pixel-wise statistics; Sex Characteristics; Template deformation morphometry; Image analysis; Multiple comparison problem; Magnetic resonance imaging; Feature extraction; Pixel-wise statistics; Corpus callosum; Dynamic recursive partitioning; Template deformation morphometry
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2009.05.007

Summary Objective We evaluate the feasibility of applying dynamic recursive partitioning (DRP), an image analysis technique, to perform morphometric analysis. We apply DRP to detect and characterize discriminative morphometric characteristics between anatomical brain structures from different groups of subjects. Our method reduces the number of statistical tests, commonly required by pixel-wise statistics, alleviating the effect of the multiple comparison problem. Methods and Materials The main idea of DRP is to partition the two-dimensional (2D) image adaptively into progressively smaller subregions until statistically significant discriminative regions are detected. The partitioning process is guided by statistical tests applied on groups of pixels. By performing statistical tests on groups of pixels rather than on individual pixels, the number of statistical tests is effectively reduced. This reduction of statistical tests restricts the effect of the multiple comparison problem (i.e., type-I error). We demonstrate an application of DRP for detecting gender-related morphometric differentiation of the corpus callosum. DRP was applied to template deformation fields computed from registered magnetic resonance images of the corpus callosum to detect regions of significant expansion or contraction between female and male subjects. Results DRP was able to detect regions comparable to those of pixel-wise analysis, while reducing the number of required statistical tests up to almost 50%. The detected regions were in agreement with findings previously reported in the literature. Statistically significant discriminative morphological variability was detected in the posterior corpus callosum region, the isthmus and the anterior corpus callosum. In addition, by operating on groups of pixels, DRP appears to be less prone to detecting spatially diffused and isolated outlier pixels as significant. Conclusion DRP can be a viable approach for detecting discriminative morphometric characteristics among groups of subjects, having the potential to alleviate the multiple comparisons’ effect by significantly reducing the number of required statistical tests.