Akan Taymaz, Oskouei Amin Golzari, Alp Sait, Bhuiyan Mohammad Alfrad Nobel
Department of Medicine, Louisiana State University Health Sciences Center, Shreveport, LA 71103, USA.
Software Engineering Department, Istanbul Topkapi University, 34020 Istanbul, Turkey.
Multimed Tools Appl. 2025 May;84(16):16971-17020. doi: 10.1007/s11042-024-19725-4. Epub 2024 Jul 2.
One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient's prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram's peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters.
医学科学界高度关注的领域之一是从脑磁共振成像（MRI）中分割肿瘤。早期诊断恶性肿瘤对于为患者提供治疗至关重要。如果能早期检测到，患者的预后将会改善。医学专家在诊断脑肿瘤时采用手动分割方法。本研究提出了一种新方法来简化和自动化这一过程。在最近的研究中，多级分割已广泛应用于医学图像分析，分割方法的有效性和精度直接与所使用的段数相关。然而，选择合适的段数通常由用户决定，并且对许多分割算法来说具有挑战性。所提出的方法是基于3D直方图的分割方法的改进版本，它可以自动确定合适的段数。一般算法包含三个主要步骤：第一步是使用高斯滤波器对图像的3D RGB直方图进行平滑处理。这消除了过于靠近的不可靠和非主导的直方图峰值。接下来，一种多模态粒子群优化方法识别直方图的峰值。最后，根据非欧几里得距离将像素放置在最符合其特征的聚类中。所提出的算法已应用于癌症成像存档（TCIA）和用于脑肿瘤检测数据集的脑MRI图像。将所提出方法的结果与三种聚类方法的结果进行比较：FCM、FCM_FWCW和FCM_FW。在对各种MRI切片的这三种算法的对比分析中。我们的算法始终表现出卓越的性能。它在所有三个指标中均取得最高平均排名，表明其在聚类方面的稳健性和有效性。所提出的方法在实验中是有效的，证明了其找到合适聚类的能力。
