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SNMQ stands for "Spatially Normalized Mutual Information." It is a computational technique used in medical image registration and analysis to align and compare images obtained from different imaging modalities or time points. Medical image registration is a fundamental task in medical image processing that involves aligning images from multiple sources to enable accurate comparison and analysis. SNMQ specifically focuses on spatially normalizing images to account for differences in spatial orientation, resolution, and anatomical variability between subjects or image acquisitions. The technique utilizes mutual information, a measure of statistical dependence between two random variables, to quantify the similarity between corresponding image regions before and after spatial normalization. By maximizing mutual information, SNMQ enables robust and accurate alignment of medical images, even in the presence of anatomical variations or imaging artifacts. This alignment facilitates the integration of information from different imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), to improve diagnostic accuracy, treatment planning, and patient monitoring in various medical applications, including neuroimaging, oncology, and cardiology. Additionally, SNMQ-based registration techniques can be combined with advanced image analysis algorithms, such as segmentation, feature extraction, and machine learning, to extract meaningful information from medical images and support clinical decision-making. As a versatile and widely used tool in medical image processing, SNMQ plays a crucial role in advancing our understanding of disease mechanisms, guiding therapeutic interventions, and improving patient outcomes in modern healthcare practice.