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Brain Lesion Segmentation of Diffusion-Weighted MRI Using Thresholding Technique

机译:基于阈值技术的弥散加权MRI脑损伤分割

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This paper presents brain lesion segmentation of diffusion-weighted magnetic resonance images (DW-MRI or DWI) based on thresholding technique. The lesions are solid tumor, acute infarction, haemorrhage, and abscess. Preprocessing is applied to the DWI for normalization, background removal and enhancement. Two different techniques which are Gamma-law transformation and contrast stretching are applied for the enhancement. For the image segmentation process, the DWI is divided by 8 × 8 regions. Then image histogram is calculated at each region to find the maximum number of pixels for each intensity level. The optimal threshold is determined by comparing normal and lesion regions. By using Gamma-law transformation, 0.48 is found as the optimal thresholding value whereas 0.28 for the contrast stretching. The proposed technique has been validated by using area overlap (AO), false positive rate (FPR), and false negative rate (FNR). Thresholding with gamma-law transformation algorithm provides better segmentation results compared to contrast stretching technique. The proposed technique provides good brain lesion segmentation results even though the simplest segmentation technique is used.
机译:本文提出了基于阈值技术的弥散加权磁共振图像(DW-MRI或DWI)的脑病变分割。病变为实体瘤,急性梗塞,出血和脓肿。预处理应用于DWI以进行标准化,背景去除和增强。伽玛定律变换和对比度拉伸这两种不同的技术被应用到增强中。对于图像分割过程,将DWI除以8×8区域。然后在每个区域计算图像直方图,以找到每个强度级别的最大像素数。最佳阈值通过比较正常区域和病变区域来确定。通过使用伽玛定律变换,可以找到0.48作为最佳阈值,而对比度对比度则为0.28。通过使用面积重叠(AO),误报率(FPR)和误报率(FNR)验证了所提出的技术。与对比度拉伸技术相比,具有伽玛定律变换算法的阈值提供了更好的分割结果。即使使用最简单的分割技术,所提出的技术也可以提供良好的脑部病变分割结果。

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