Subudhi Badri Narayan, Thangaraj Veerakumar, Sankaralingam Esakkirajan, Ghosh Ashish
Department of Electronics and Communication Engineering, National Institute of Technology Goa, Farmagudi, Ponda, Goa, 403401, India.
Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, India.
Magn Reson Imaging. 2016 Nov;34(9):1292-1304. doi: 10.1016/j.mri.2016.07.002. Epub 2016 Jul 28.
In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures.
在本文中，提出了一种基于统计融合的分割技术，用于识别磁共振图像（MRI）中的不同异常情况。所提出的方案包括种子选择、区域生长合并以及多个图像段的融合。在此过程中，首先将图像划分为多个块，并为每个块计算傅里叶变换的相位分量。每个块的相位分量反映了块内的灰度变化，但它们之间存在很大的相关性。因此，采用奇异值分解（SVD）技术来生成每个块的奇异值。然后对这些奇异值应用阈值处理，以识别边缘区域和平滑区域，并选择一些种子点进行分割。通过考虑每个种子点，对整个MRI进行二进制分割，因此利用所有种子点我们得到了相同数量的二进制图像。基于区域的统计融合过程用于将所有二进制图像融合成多个段。所提出方案在识别不同异常情况方面的有效性进行了测试：前列腺癌检测、结核性肉芽肿识别以及颅内肿瘤或脑肿瘤检测。通过将其结果与七种先进技术进行比较，并采用六种性能评估指标，验证了所提出的技术。
