Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which complicate conventional membrane-based segmentation. We found that many recent approaches rely on heavy neural architectures and extensive training, but still struggle to generalize across staining and acquisition variability. To address these limitations, we propose the Perinuclear Ring-based Image Segmentation Method (PRISM), which replaces explicit cytoplasmic delineation with adaptive concentric zones constructed around the nucleus. These perinuclear regions enable the extraction of robust cytoplasmic descriptors by integrating color information with texture statistics derived from grey-level co-occurrence patterns, without requiring accurate cell-boundary detection. A calibrated stacking ensemble of traditional classifiers leverages these descriptors to achieve a high performance, with an accuracy of 98.46% and a precision-recall AUC of 0.9937.
@article{arxiv.2605.12851,
title = {PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification},
author = {Larissa Ferreira Rodrigues Moreira and Leonardo Gabriel Ferreira Rodrigues and Rodrigo Moreira and André Ricardo Backes},
journal= {arXiv preprint arXiv:2605.12851},
year = {2026}
}
Comments
Paper accepted for publication at the XXVI Simp\'osio Brasileiro de Computa\c{c}\~ao Aplicada \`a Sa\'ude (SBCAS 2026), Ouro Preto, MG, Brazil