This paper introduces a methodology for quantitative breast MRI tissue segmentation developed in Python, designed to accurately differentiate fibroglandular, fatty, and pathological regions while minimizing observer variability. The modular application supports DICOM/TIFF and multi-sequence data (T1-W, T2-W, DCE). The five-step pipeline integrates preprocessing with noise reduction (Bilateral, NLM, or Median filters), multi-algorithm segmentation (e.g., K-means, GMM, Watershed), and automatic tissue classification. Initial promising results were obtained on patient data, with future efforts focusing on combining segmentation methods for further accuracy improvement and a direct STL export feature.