Brain Tumor Segmentation of T1w MRI Images Based on Clustering Using Dimensionality Reduction Random Projection Technique
Brain Tumor Segmentation of T1w MRI Images Based on Clustering Using Dimensionality Reduction Random Projection Technique
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DOI：
10.2174/1573405616666200712180521
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发表时间：
2021-01-01
影响因子：
1.4
通讯作者：
Prasad, K. Satya
中科院分区：
文献类型：
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作者：
Babu, K. Rajesh;Nagajaneyulu, P., V;Prasad, K. Satya
背景：脑肿瘤的早期诊断可能会延长预期寿命。磁共振成像（MRI）伴随着几种分割算法是首选的可靠的评估方法。诊断过程中高维医学图像数据的可用性带来了沉重的计算负担，并且需要适当的预处理步骤来进行低维表示。图像数据的存储要求和复杂性也是一个问题。为了解决这个问题，随机投影技术（RPT）被广泛使用的数据reduction. Objective多元方法：本研究主要集中在T1加权MRI图像聚类脑肿瘤分割与降维，通过使用传统的主成分分析（PCA）和RPT。方法：两种聚类算法，K-均值和模糊C-均值（FCM）用于脑肿瘤检测。主要研究目的是比较两种聚类方法的MRI图像进行PCA和RPT。除了512 × 512的原始尺寸，其他三个图像大小，256 × 256，128 × 128，和64 × 64，被用来确定的效果的methods.Results：在平均重建，欧氏距离和分割距离误差方面，RPT产生更好的结果比PCA方法的聚类技术的所有聚类图像。根据性能指标的值，RPT支持模糊c-均值在实现最佳的聚类性能，并提供了显着的结果，为每个新的大小的MRI图像。
Background: Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. The availability of high-dimensional medical image data during diagnosis places a heavy computational burden and a suitable pre-processing step is required for lower-dimensional representation. The storage requirement and complexity of image data are also a concern. To address this concern, the random projection technique (RPT) is widely used as a multivariate approach for data reduction.Aim: This study mainly focuses on T1-weighted MRI image clustering for brain tumor segmentation with dimension reduction by using the conventional principal component analysis (PCA) and RPT.Methods: Two clustering algorithms, K-means and fuzzy c-means (FCM) were used for brain tumor detection. The primary study objective was to present a comparison of the two clustering methods between MRI images subjected to PCA and RPT. In addition to the original dimension of 512 x 512, three other image sizes, 256 x 256, 128 x 128, and 64 x 64, were used to determine the effect of the methods.Results: In terms of average reconstruction, Euclidean distance, and segmentation distance errors, the RPT produced better results than the PCA method for all the clustered images from clustering techniques. According to the values of performance metrics, RPT supported fuzzy c-means in achieving the best clustering performance and provided significant results for each new size of the MRI images.