Raster masks—binary, object-based, or energy field masks—are generated by identifying informative features within images. To facilitate transparent and interpretable representation of these features, we propose a two-stage vectorization approach. In the primary transformation, we extract points, lines, and polygons from rasterized regions, creating contours and shape paths that define object boundaries. The secondary transformation applies morphological operations to the resulting vector data to cluster, classify, and simplify object geometry, enhancing interpretation and visualization. Our method processes linear rasters derived from connected component boundaries in binary masks, object-based masks detected via gradient edge operators, and energy field masks segmented by thresholding energy values. Building on classical digital image processing techniques, we developed algorithms for advanced morphological operations on extracted contours. These algorithms were validated on a diverse set of high-resolution images and corresponding raster masks, demonstrating improved accuracy in delineating and simplifying complex shapes. The outcome of this work is a specialized library for morphological processing of contours, enabling efficient and precise analysis of vectorized features across various imaging applications. This approach provides a foundation for robust object detection and characterization in computer vision tasks, particularly in fields requiring high-resolution, detail-preserving analysis, such as remote sensing and medical imaging.