PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

• Larissa Ferreira Rodrigues Moreira UFV
• Leonardo Gabriel Ferreira Rodrigues UFU
• Rodrigo Moreira UFV
• André Ricardo Backes UFSCar

DOI: https://doi.org/10.5753/sbcas.2026.20537

Resumo

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.

Referências

Aby, A. E., Salaji, S., Anilkumar, K., and Rajan, T. (2024). A review on leukemia detection and classification using artificial intelligence-based techniques. Computers and Electrical Engineering, 118:109446.

Baldin, I., Nikolich, A., Griffioen, J., Monga, I. I. S., Wang, K.-C., Lehman, T., and Ruth, P. (2019). FABRIC: A national-scale programmable experimental network infrastructure. IEEE Internet Computing, 23(6):38–47.

Das, P. K. and Meher, S. (2021). An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia. Expert Systems with Applications, 183:115311.

Das, P. K., Meher, S., Rath, A., and Panda, G. (2025). An efficient deep learning system for automatic detection of acute lymphoblastic leukemia. ISA Transactions, 158:488–496.

de Faria, L. C., Rodrigues, L. F., and Mari, J. F. (2018). Cell classification using hand-crafted features and bag of visual words. In Anais do XIV Workshop de Visão Computacional, pages 68–73.

Dias Júnior, D., Cruz, L., Diniz, J., Júnior, G. B., and Silva, A. (2021). Classificação automática de glóbulos brancos usando descritores de forma e textura e extreme gradient boosting. In Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde, pages 95–106, Porto Alegre, RS, Brasil. SBC.

Hita, X., Javed, F., and Lodi, S. (2026). Reliable leukemia detection via transfer-enhanced bayesian cnns. Computers in Biology and Medicine, 202:111419.

Labati, R. D., Piuri, V., and Scotti, F. (2011). All-idb: The acute lymphoblastic leukemia image database for image processing. In 2011 18th IEEE International Conference on Image Processing, pages 2045–2048.

Oybek Kizi, R. F., Theodore Armand, T. P., and Kim, H.-C. (2025). A review of deep learning techniques for leukemia cancer classification based on blood smear images. Applied Biosciences, 4(1).

Rodrigues, L. F., Backes, A. R., Travençolo, B. A. N., and de Oliveira, G. M. B. (2022). Optimizing a deep residual neural network with genetic algorithm for acute lymphoblastic leukemia classification. Journal of Digital Imaging, 35(3):623–637.

Rodrigues, L. F., Silva, J. H., Gondim, P. H. C. C., and Mari, J. F. (2016). Leukocytes classification in microscopy images for acute lymphoblastic leukemia identification. In XII Workshop de Visão Computacional, pages 68–73.

Rodrigues Moreira, L. F., Moreira, R., Travençolo, B. A. N., and Backes, A. R. (2025). Deep learning based image classification for embedded devices: A systematic review. Neurocomputing, 623:129402.

Sahlol, A. T., Abdeldaim, A. M., and Hassanien, A. E. (2019). Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm. Soft Computing, 23(15):6345–6360.

Sahlol, A. T., Ismail, F. H., Abdeldaim, A., and Hassanien, A. E. (2017). Elephant herd optimization with neural networks: A case study on acute lymphoblastic leukemia diagnosis. In 2017 12th International Conference on Computer Engineering and Systems (ICCES), pages 657–662.

Sahlol, A. T., Kollmannsberger, P., and Ewees, A. A. (2020). Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Scientific Reports, 10(1):2536.

Scotti, F. (2005). Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005., pages 96–101.

Zolfaghari, M. and Sajedi, H. (2022). A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells. Multimedia Tools and Applications, 81(5):6723–6753.