Department of Computer Science, COMSATS University Islamabad, Wah Campus, GT Road Wah Cantt, Punjab 47040, Pakistan.
Department of Computer Science, COMSATS University Islamabad, Wah Campus, GT Road Wah Cantt, Punjab 47040, Pakistan.
Comput Methods Programs Biomed. 2019 Aug;177:69-79. doi: 10.1016/j.cmpb.2019.05.015. Epub 2019 May 17.
Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumor detection using Magnetic Resonance Imaging (MRI) may increase patient's survival rate. In MRI, tumor is shown more clearly that helps in the process of further treatment. This work aims to detect tumor at an early phase.
In this manuscript, Weiner filter with different wavelet bands is used to de-noise and enhance the input slices. Subsets of tumor pixels are found with Potential Field (PF) clustering. Furthermore, global threshold and different mathematical morphology operations are used to isolate the tumor region in Fluid Attenuated Inversion Recovery (Flair) and T2 MRI. For accurate classification, Local Binary Pattern (LBP) and Gabor Wavelet Transform (GWT) features are fused.
The proposed approach is evaluated in terms of peak signal to noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) yielding results as 76.38, 0.037 and 0.98 on T2 and 76.2, 0.039 and 0.98 on Flair respectively. The segmentation results have been evaluated based on pixels, individual features and fused features. At pixels level, the comparison of proposed approach is done with ground truth slices and also validated in terms of foreground (FG) pixels, background (BG) pixels, error region (ER) and pixel quality (Q). The approach achieved 0.93 FG and 0.98 BG precision and 0.010 ER on a local dataset. On multimodal brain tumor segmentation challenge dataset BRATS 2013, 0.93 FG and 0.99 BG precision and 0.005 ER are acquired. Similarly on BRATS 2015, 0.97 FG and 0.98 BG precision and 0.015 ER are obtained. In terms of quality, the average Q value and deviation are 0.88 and 0.017. At the fused feature based level, specificity, sensitivity, accuracy, area under the curve (AUC) and dice similarity coefficient (DSC) are 1.00, 0.92, 0.93, 0.96 and 0.96 on BRATS 2013, 0.90, 1.00, 0.97, 0.98 and 0.98 on BRATS 2015 and 0.90, 0.91, 0.90, 0.77 and 0.95 on local dataset respectively.
The presented approach outperformed as compared to existing approaches.
脑肿瘤是由于细胞异常发育引起的。它是全球成年人死亡的主要原因之一。通过早期发现脑肿瘤，可以预防数百万人死亡。早期使用磁共振成像（MRI）检测脑肿瘤可以提高患者的生存率。在 MRI 中，肿瘤显示得更清楚，有助于进一步治疗。这项工作旨在早期检测肿瘤。
在本文中，我们使用 Wiener 滤波器与不同的小波带进行去噪和增强输入切片。使用势场（PF）聚类找到肿瘤像素子集。此外，全局阈值和不同的数学形态学操作用于在液体衰减反转恢复（Flair）和 T2 MRI 中分离肿瘤区域。为了进行准确的分类，融合了局部二值模式（LBP）和 Gabor 小波变换（GWT）特征。
该方法在峰值信噪比（PSNR）、均方误差（MSE）和结构相似性指数（SSIM）方面进行了评估，在 T2 上的结果分别为 76.38、0.037 和 0.98，在 Flair 上的结果分别为 76.2、0.039 和 0.98。分割结果基于像素、单个特征和融合特征进行评估。在像素水平上，将所提出的方法与真实切片进行了比较，并根据前景（FG）像素、背景（BG）像素、误差区域（ER）和像素质量（Q）进行了验证。该方法在局部数据集上实现了 0.93 FG 和 0.98 BG 精度和 0.010 ER。在多模态脑肿瘤分割挑战数据集 BRATS 2013 上，获得了 0.93 FG 和 0.99 BG 精度和 0.005 ER。类似地，在 BRATS 2015 上，获得了 0.97 FG 和 0.98 BG 精度和 0.015 ER。在质量方面，平均 Q 值和偏差分别为 0.88 和 0.017。在基于融合特征的水平上，BRATS 2013 的特异性、灵敏度、准确性、曲线下面积（AUC）和骰子相似系数（DSC）分别为 1.00、0.92、0.93、0.96 和 0.96，BRATS 2015 分别为 0.90、1.00、0.97、0.98 和 0.98，本地数据集分别为 0.90、0.91、0.90、0.77 和 0.95。
与现有方法相比，所提出的方法表现更好。
