This enquiry portrays the operational metrics of Python libraries (library) for said Digital Imaging and Communications in Medicine (DICOM) image processing, overlapping pydicom (scikit-image and OpenCV integrated) with SimpleITK. Thus, a central enquiry therefor entails the following: Estimating said operational efficacy across file loading via processing its duration and memory consumption. Next, we have "empirical trials": These empirical trials are prompted upon a veritable DICOM corpus, demonstrating pydicom with OpenCV, decisively, exceeding SimpleITK in both ingest and computational velocity. However, memory retention remains correlated therewith. These outcomes are insightfully revealing for architects and investigators, navigating such judicious library selections concerning medical imaging applications, which, in effect, exact computational efficiency................ Keywords:............ DICOM- Digital Imaging and Communications in Medicine, pydicom-Python_Dicom, OpebCV- Open-Source Computer Vision Library, Simplified Insight Segmentation and Registration Toolkit.