School of Mathematics, Iran University of Science and Technology, Narmak, Tehran, 1684613114, Tehran, Iran.
J Digit Imaging. 2022 Dec;35(6):1421-1432. doi: 10.1007/s10278-022-00653-4. Epub 2022 May 31.
For many years, brain tumor detection has been one of the most essential and competitive issues for medical researchers. Many methods have been developed to detect normal and abnormal tissues in Magnetic Resonance (MR) images. In this work, we present a novel algorithm based on iterative Co-Clustering and K-Means (ICCK). After image pre-processing and enhancement, this algorithm recognizes the part of the image that contains the tumor and eliminates the unused parts using a modification of the Co-Clustering method. Finally, the K-Means clustering method is adopted to detect the tumor area. The Co-Clustering methods cannot be used directly for the detection of brain tumors because they manipulate the image matrix for the purpose of block clustering. Furthermore, they are incapable of detecting the tumor area correctly and accurately. Such issues are addressed by our proposed methodology. The latent block model (LBM) is applied as the Co-Clustering method in this work. We evaluate the performance of our method on the images that were collected from the BraTS2019 dataset. The sensitivity, specificity, accuracy, and dice similarity coefficient values for our method are 82.41%, 99.74%, 99.28%, and 84.87%, respectively, which shows that the proposed method outperforms the existing methods in the literature. Moreover, it performs much better on complex images.
多年来，脑肿瘤检测一直是医学研究人员最关注和最具竞争力的问题之一。已经开发了许多方法来检测磁共振（MR）图像中的正常和异常组织。在这项工作中，我们提出了一种基于迭代协同聚类和 K-Means（ICCK）的新算法。在图像预处理和增强之后，该算法使用协同聚类方法的修改来识别包含肿瘤的图像部分并消除未使用的部分。最后，采用 K-Means 聚类方法检测肿瘤区域。协同聚类方法不能直接用于脑肿瘤的检测，因为它们操纵图像矩阵的目的是进行块聚类。此外，它们无法正确和准确地检测肿瘤区域。我们提出的方法解决了这些问题。本工作采用潜在块模型（LBM）作为协同聚类方法。我们在从 BraTS2019 数据集收集的图像上评估了我们方法的性能。我们方法的灵敏度、特异性、准确性和骰子相似系数值分别为 82.41%、99.74%、99.28%和 84.87%，这表明所提出的方法优于文献中的现有方法。此外，它在复杂图像上的表现要好得多。
