Image processing and visualization of medical data have become critical in supporting clinical diagnosis and treatment planning. To facilitate the development of medical imaging applications, open-source libraries such as the Insight Registration and Segmentation Toolkit (ITK) and the Visualization Toolkit (VTK) are available. These libraries provide a comprehensive set of tools and algorithms for image registration, segmentation, and visualization. In addition, the flexibility of these libraries allows for the creation of custom user interfaces, enabling the rapid development of medical imaging applications tailored to specific needs. The direct integration of ITK and VTK classes using Python offers significant advantages for rapid development of medical applications and teaching of medical image analysis. In this paper, we present an optimized approach for 3D segmentation of brain tumors using DICOM files, leveraging the capabilities of the VTK and ITK toolkits. The proposed method aims to improve the accuracy and efficiency of tumor segmentation in medical imaging data. The effectiveness of this technique was successfully verified using the given image. Through rigorous testing and evaluation, the proposed approach demonstrated its ability to accurately and reliably perform the intended task on the provided picture.