NONE 20260511091709427 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 1109-9518 2224-2902 Emanuela Genovese Department of Civil Engineering, Energy, Environment and Materials (DICEAM) Mediterranea University of Reggio Calabria Via Zehender, 89124 Reggio Calabria ITALY Elena Barrile Department of Nervous and Mental Diseases La Sapienza University of Rome Via Eudossiana, 18, 00184 Roma ITALY Davide Chiffi Department of Nervous and Mental Diseases La Sapienza University of Rome Via Eudossiana, 18, 00184 Roma ITALY Vincenzo Barrile Department of Civil Engineering, Energy, Environment and Materials (DICEAM) Mediterranea University of Reggio Calabria Via Zehender, 89124 Reggio Calabria ITALY Medical image segmentation is currently performed by qualified medical personnel who, however, often must deal with large amounts of work, often in emergencies. Therefore, there is a need for automatic and reliable methods to analyze medical images, particularly magnetic resonance imaging, with high precision. There are several methods in the literature that are mainly based on the use of 3D atlases or convolutional neural networks, subject to several disadvantages. The present research work focuses on the development of an alternative and innovative method for the segmentation of medical images using a bilateral filter, a genetic programming algorithm developed ad hoc, the CLAHE algorithm, and the Watershed segmentation algorithm, typically used for geo-topographic images. The results obtained by the following methodology allow us to conclude that the proposed approach provides comparable results with a smaller dataset of images compared to the most common neural networks used in the field. The integration of the genetic algorithm represents a highly innovative solution, improving the local quality of the contrast and reducing the noise of the images, and allowing for optimizing the Watershed segmentation. 05 11 2026 05 11 2026 169 15 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23208.2026.23.15 https://wseas.com/journals/bab/2026/a305108-010(2026).pdf Sahu, S., Singh, A.K. Genetic algorithm based multi-resolution approach for de-speckling OCT image. Multimed Tools Appl 83, 31081–31102 (2024). DOI: 10.1007/s11042-023-16575-4 Ezhilarasan, K., Somasundaram, K., Kalaiselvi, T., Somasundaram, P., Selvi, S. K., & Jeevarekha, A. (2024). Bio-Inspired Algorithms Used in Medical Image Processing. In BioInspired Optimization Techniques in Blockchain Systems (pp. 25-60). IGI Global Scientific Publishing. DOI: 10.4018/979-8-3693-1131- 8.ch002. Bhardwaj, A., Kaur, S., Shukla, A. P., & Shukla, M. K. (2019, March). An enhanced cellular automata based filter for despeckling of ultrasound images. In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India. (pp. 1095-1099). IEEE. DOI: 10.1109/SPIN.2019.8711772 Korevaar, S., Tennakoon, R., & Bab-Hadiashar, A. (2024). Generalization Capabilities of Neural Cellular Automata for Medical Image Segmentation: A Robust and Lightweight Approach. arXiv preprint arXiv:2408.15557. Bhardwaj, A., Kaur, S., Shukla, M., Shukla, A.P. (2022). A Despeckling Filter for Ultrasound Images Based on Cellular Automata Approach. In: Reddy, A.N.R., Marla, D., Favorskaya, M.N., Satapathy, S.C. (eds) Intelligent Manufacturing and Energy Sustainability. Smart Innovation, Systems and Technologies, vol 265. Springer, Singapore. DOI: 10.1007/978-981-16-6482-3_22 Fajardo-Delgado, D., Rodríguez-González, A. Y., Sandoval-Pérez, S., Molinar-Solís, J. E., & Sánchez-Cervantes, M. G. (2023). Genetic Programming to Remove Impulse Noise in Color Images. Applied Sciences, 14(1), 126. DOI: 10.3390/app14010126 Vanneschi, L., & Poli, R. (2012). Genetic Programming-Introduction, Applications, Theory and Open Issues. Handbook of natural computing, 2, 709-739. DOI: 10.1007/978-3- 540-92910-9_24 Javed, S. G., Majid, A., & Lee, Y. S. (2018). Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images. Multimedia Tools and Applications, 77, 15657-15675. DOI: 10.1007/s11042-017-5139- 2 Khan, S. U., Ullah, N., Ahmed, I., Chai, W. Y., & Khan, A. (2018). MRI images enhancement using genetic programming based hybrid noise removal filter approach. Current Medical Imaging Reviews, 14(6), 867-873. DOI: 10.2174/1573405613666170619093021 Cortacero, K., McKenzie, B., Müller, S., Khazen, R., Lafouresse, F., Corsaut, G., ... & Cussat-Blanc, S. (2023). Evolutionary design of explainable algorithms for biomedical image segmentation. Nature communications, 14(1), 7112. DOI: 10.1038/s41467-023-42664-x Poli, R. (1996, April). Genetic programming for feature detection and image segmentation. In AISB Workshop on Evolutionary Computing (pp. 110-125). Berlin, Heidelberg: Springer Berlin Heidelberg. DOI: 10.1007/BFb0032777 Dhot, T. S. (2009). GPIS: genetic programming based image segmentation with applications to biomedical object detection (Doctoral dissertation, Concordia University). Ramesh, N., Ashfaq, T., & Kharma, N. (2024, September). A Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical Image Segmentation. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 87-98). Cham: Springer Nature Switzerland. DOI: 10.1007/978-3-031-71602-7_8 Kumar Rai, R., Gour, P., & Singh, B. (2012). Underwater image segmentation using clahe enhancement and thresholding. International Journal of Emerging Technology and Advanced Engineering, 2(1), 118-123. https://api.semanticscholar.org/CorpusID:1470 8740 (Last Accessed on 17 February 2025) Intyanto, G. W., Apriyanto, R., Anam, K., Chaidir, A. R., Sarwono, C. S., & Eska, A. C. (2024, November). UGER: Enhancing the Quality of ROUV Image Segmentation with CLAHE and Color Space-Morphology Masking Techniques. In 2024 IEEE 2nd International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT) (pp. 1-5), Jember, Indonesia. IEEE. DOI: 10.1109/ICEECIT63698.2024.10859578 Barrile, V., Cotroneo, F., Genovese, E., Barrile, E., and Bilotta, G.: An AI Segmenter On Medical Imaging For Geomatics Applications Consisting Of A Two-State Pipeline, Snns Network And Watershed Algorithm, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W3-2023, 21–26, DOI: 10.5194/isprs-archives-XLVIII-2-W3-2023-21- 2023, 2023 Fajardo-Delgado, D., Sánchez, M. G., MolinarSolis, J. E., Fernandez-Zepeda, J. A., Vidal, V., & Verdiú, G. (2016, July). A hybrid genetic algorithm for color image denoising. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 3879-3886),Vancouver, BC, Canada. IEEE. DOI: 10.1109/CEC.2016.7744281 Awarayi, N. S., Twum, F., Hayfron-Acquah, J. B., & Owusu-Agyemang, K. (2024). A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN. Plos one, 19(4), e0302358. DOI: 10.1371/journal.pone.0302358 Manar, A. A., & Zohair, A. A. (2024). Sharpness Improvement Of Magnetic Resonance Images Using A Guided-Subsumed Unsharp Mask Filter. Applied Computer Science, 20(4), 192- 210. DOI: 10.35784/acs-2024-47 Kumar, A., Vishwakarma, A., & Bajaj, V. (2024). Multi-headed CNN for colon cancer classification using histopathological images with tikhonov-based unsharp masking. Multimedia Tools and Applications, 83(28), 71753-71772. DOI: 10.1007/s11042-024-18357-y Hassan, M., Sateesan, A., Vliegen, J., Picek, S., & Mentens, N. (2024). A Genetic Programming approach for hardware-oriented hash functions for network security applications. Applied Soft Computing, 165, 112078. DOI: 10.1016/j.asoc.2024.112078 Buriboev, A. S., Khashimov, A., Abduvaitov, A., & Jeon, H. S. (2024). CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm. Sensors, 24(23), 7703. DOI:10.3390/s24237703 Saifullah, S., Suryotomo, A. P., Dreżewski, R., Tanone, R., & Tundo, T. (2024, February). Optimizing brain tumor segmentation through CNN U-Net with CLAHE-HE image enhancement. In 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023) (pp. 90-101). Atlantis Press. Yogyakarta, Indonesia. DOI: 10.2991/978-94-6463-366-5_9 Yogalakshmi, G., & Rani, B. S. (2024). Sailfish optimizer based CLAHE with U-NET for MRI brain tumour segmentation. Measurement: Sensors, 33, 101229. DOI: 10.1016/j.measen.2024.101229 Yoshimi, Y., Mine, Y., Ito, S., Takeda, S., Okazaki, S., Nakamoto, T., ... & Tanimoto, K. (2024). Image preprocessing with contrastlimited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on magnetic resonance images. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 138(1), 128- 141. DOI: 10.1016/j.oooo.2023.01.016 Liu, Y., Chen, D., Fu, S., Mathiopoulos, P. T., Sui, M., Na, J., & Peethambaran, J. (2024). Segmentation of individual tree points by combining marker-controlled watershed segmentation and spectral clustering optimization. Remote Sensing, 16(4), 610. DOI: 10.3390/rs16040610 Annavarapu, A., & Borra, S. (2024). An adaptive watershed segmentation based medical image denoising using deep convolutional neural networks. Biomedical Signal Processing and Control, 93, 106119. DOI: 10.1016/j.bspc.2024.106119 Li, Y., Zhou, L., Wen, X. L., Wu, T. Y., Lan, M., Zhang, D., ... & De Lai, P. (2024). Tree barriers height information extraction under power line based on watershed segmentation and local maximum algorithm. International Journal of Remote Sensing, 45(7), 2424-2443. DOI: 10.1080/01431161.2024.2331975 Choudhary, N., Rathore, P. S., & Kumar, D. (2024, April). Comparative Evaluation of Marker-Controlled Method and Gradient Distance Transformation through Watershed Segmentation. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-6), Pune, India. IEEE. DOI: 10.1109/I2CT61223.2024.10543576 He, P., Shen, T., Wang, Y., Zhu, D., Hu, Q., Li, H., ... & Yang, A. (2025). A study on automatic annotation methods for watershed environmental elements based on semantic segmentation models. European Journal of Remote Sensing, 58(1), 2473939. DOI: 10.1080/22797254.2025.2473939 Zhang, J., Li, Z., Yang, J., Dong, Y., & Zhao, M. (2024, July). Winter wheat classification within heterogeneous agricultural landscapes. In 2024 12th International Conference on AgroGeoinformatics (Agro-Geoinformatics) (pp. 1- 5), Novi, Sad, Serbia. IEEE. DOI: 10.1109/AgroGeoinformatics262780.2024.10660674 Barrile, V., Bilotta, G., An application of remote sensing: Object-oreinted analysis of satellite data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 37 (Part B8), 1047-114. https://www.isprs.org/proceedings/XXXVII/co ngress/8_pdf/1_WG-VIII-1/20.pdf (Last Accessed on 15 January 2025) Barrile, V., Bernardo, E., Fotia, A., & Bilotta, G. (2022). Integration of laser scanner, groundpenetrating radar, 3D models and mixed reality for artistic, archaeological and cultural heritage dissemination. Heritage, 5(3), 1529-1550. DOI: 10.3390/heritage5030080 Human Heart Project. https://humanheartproject.creatis.insalyon.fr/database/#collection/6373703d73e9f004 7faa1bc8 (Last Accessed on 28 April 2025) Mohamed, I. F. (2025). Molecular Mechanisms Of Disinfectant-Induced Cross-Resistance In Neonatal Care Units-Associated Escherichia Coli. WSEAS Transactions on Biology and Biomedicine, 22(1), 14-26. https://wseass.com/index.php/bab/article/view/ 16 (Last Accessed on 12 April 2025) Srisabarimani, K., & Arthi, R. (2023). Deep learning based brain stroke detection using improved VGGNet. WSEAS Transactions on Biology and Biomedicine, 20, 204-212. https://www.wseas.com/journals/bab/2023/a42 5108-017(2023).pdf (Last Accessed on 12 April 2025) Dhananjaya, V., & Geetha, M. (2022). Effective analysis and accurate detection of common diseases in ECG signals and classifications and monitoring through cloud computing technology. WSEAS Transactions on Biology and Biomedicine, 19, 107-117. https://wseas.com/journals/bab/2022/a265108- 010(2022).pdf (Last Accessed on 12 April 2025) Martusevich, A. K., Nazarov, V. V., Petrov, S. V., Kondratyeva, T. B., Khlevchuk, T. V., Badyanova, V. S., & Peretyagin, S. P. (2025). Near-field Resonance Microwave Imaging for Fibrosis Detection in Patients with Dupuytren Disease. WSEAS Transactions on Biology and Biomedicine, 22, 415-419. https://www.wseas.com/journals/bab/2025/a76 5108-030(2025).pdf (Last Accessed on 14 April 2025)