Murugan K, Palanisamy SatheeshKumar, Sathishkumar N, Alshalali Tagrid Abdullah N
Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, 641407, India.
School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Sci Rep. 2025 Jul 15;15(1):25666. doi: 10.1038/s41598-025-09638-z.
Brain tumor segmentation plays a crucial role in clinical diagnostics and treatment planning, yet accurate and efficient segmentation remains a significant challenge due to complex tumor structures and variations in imaging modalities. Multi-feature selection and region classification depend on continuous homogeneous features to improve the precision of tumor detection. This classification is required to suppress the discreteness across various extraction rates to consent to the smallest segmentation region that is infected. This study proposes a Finite Segmentation Model (FSM) with Improved Classifier Learning (ICL) to enhance segmentation accuracy in Positron Emission Tomography (PET) images. The FSM-ICL framework integrates advanced textural feature extraction, deep learning-based classification, and an adaptive segmentation approach to differentiate between tumor and non-tumor regions with high precision. Our model is trained and validated on the Synthetic Whole-Head Brain Tumor Segmentation Dataset, consisting of 1000 training and 426 testing images, achieving a segmentation accuracy of 92.57%, significantly outperforming existing approaches such as NRAN (62.16%), DSSE-V-Net (71.47%), and DenseUNet+ (83.93%). Furthermore, FSM-ICL enhances classification precision to 95.59%, reduces classification error to 5.67%, and minimizes classification time to 572.39 ms, demonstrating a 10.09% improvement in precision and a 10.96% boost in classification rates over state-of-the-art methods. The hybrid classifier learning approach effectively addresses segmentation discreteness, ensuring continuous and discrete tumor region detection with superior feature differentiation. This work has significant implications for automated tumor detection, personalized treatment strategies, and AI-driven medical imaging advancements. Future directions include incorporating micro-segmentation and pre-classification techniques to further optimize performance in dense pixel-packed datasets.
脑肿瘤分割在临床诊断和治疗规划中起着至关重要的作用，但由于肿瘤结构复杂以及成像方式的差异，准确而高效的分割仍然是一项重大挑战。多特征选择和区域分类依赖于连续的同质特征来提高肿瘤检测的精度。这种分类需要抑制不同提取率之间的离散性，以符合受感染的最小分割区域。本研究提出了一种具有改进分类器学习（ICL）的有限分割模型（FSM），以提高正电子发射断层扫描（PET）图像中的分割精度。FSM-ICL框架集成了先进的纹理特征提取、基于深度学习的分类和自适应分割方法，以高精度区分肿瘤和非肿瘤区域。我们的模型在合成全脑肿瘤分割数据集上进行训练和验证，该数据集由1000张训练图像和426张测试图像组成，分割准确率达到92.57%，显著优于现有方法，如NRAN（62.16%）、DSSE-V-Net（71.47%）和DenseUNet+（83.93%）。此外，FSM-ICL将分类精度提高到95.59%，将分类误差降低到5.67%，并将分类时间缩短到572.39毫秒，与最先进的方法相比，精度提高了10.09%，分类率提高了10.96%。混合分类器学习方法有效地解决了分割离散性问题，确保了连续和离散肿瘤区域的检测，并具有卓越的特征区分能力。这项工作对自动肿瘤检测、个性化治疗策略以及人工智能驱动的医学成像进展具有重要意义。未来的方向包括纳入微分割和预分类技术，以进一步优化在密集像素填充数据集中的性能。
