Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.

中文翻译：

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结合超像素，监督学习和随机游走的神经胶质瘤分割方法

当前，病理患者的磁共振成像（MRI）脑图像分析是手动执行的，用于识别脑部结构或病变及其特征。为了可靠地诊断患者的状况，医生有时会在解释这些图像时遇到困难。这是由于难以检测病变尤其是神经胶质瘤的性质。脑胶质瘤是最常见的肿瘤之一，并且由于其形状，不规则性和不明确的局限性也是最难检测的肿瘤之一。这些肿瘤的分割是对其分类和手术计划最关键的步骤之一。本文提出了一种新的，准确的自动方法，可以对早期神经胶质瘤（良性肿瘤）进行精确分割，结合了随机游走（RW）算法和简单的线性迭代聚类算法。该研究分四个步骤进行。第一步包括将图像分解成超像素以获得肿瘤的初始轮廓。使用SLIC算法生成超像素。第二步，对于每个超像素，计算出一组统计和多重分形特征（灰度共生矩阵，多重分形趋势下降移动平均值）。第三步，使用监督随机森林（RF）类型分类器将超像素分类为健康或肿瘤性脑组织。在最后一步中，使用定制的RW算法增强了检测到的肿瘤的轮廓。使用脑肿瘤图像分割挑战2013数据库对提出的方法进行了评估。