Khemaratne, TMalasinghe, L2026-05-152025-09-092961-5011https://rda.sliit.lk/handle/123456789/4986Bloodstain Pattern Analysis (BPA) is a vital component in forensic investigations that aids in reconstructing the sequence of events at a crime scene. It is centralized in and revolves around the categorization of the patterns based on their features, as this is the most significant and critical stage of BPA. Therefore, a preliminary measure of BPA is via the thorough evaluation of images photographed of the crime scene to collect evidence as much as possible to arrive at the correct conclusion and to deduce the relevant details accurately. However, currently existing BPA methods are vulnerable to subjectivity, hence which can lead to pre-assumptions, without thoroughly and completely observing the crime scene, and consequently cause the arrival of incorrect conclusions and discrepancies in BP feature classification. Additionally, other flaws such as unintentional crime scene contamination and evidence tampering exist in these current methods as well. Henceforth, it is imperative that a novel method is constructed to eliminate these issues and arrive at the correct conclusions. This study introduces a robust image-processing-based methodology for extracting and quantifying bloodstain pattern features, thereby enhancing objectivity and reducing human error. The proposed technique encompasses critical stages: image acquisition, preprocessing, segmentation, feature extraction, and analysis. Through the use of image enhancement and segmentation algorithms, essential attributes such as impact angles, tail-to-body ratios, shape irregularities, and distribution densities are computed. The results were validated against original findings and show close agreement in feature values such as convergence area and circularity. The approach demonstrates the potential to integrate with existing BPA tools, facilitating automated, accurate, and reproducible forensic analysis.enImage processingForensic scienceBloodstain pattern analysisFeature extractionClassification algorithmImpact angleConference Paperhttps://doi.org/10.54389/LGKX8150