基于掩模的MRI病变图像分割

A. De, R. Das, A. Bhattacharjee, D. Sharma
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引用次数: 21

摘要

我们设计了一种新的技术来分割病变MRI图像,其中病变部分使用基于掩模的阈值分割技术和熵最大化分离。利用粒子群优化技术(PSO)获得MRI图像的感兴趣区域(ROI)。使用的掩码是可变掩码。矩形掩模的生长使用在随后的章节中提供的算法,使用邻近像素的相似性。对各种病变MRI图像的测试表明,无论背景的复杂性、强度水平和类大小的差异,都可以成功地提取出小的病变物体。以前的作品是基于双峰图像,而我们的工作是基于多峰图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Masking Based Segmentation of Diseased MRI Images
We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent sections using similarity of neighbourhood pixels. Tests on various diseased MRI images show that small diseased objects are successfully extracted irrespective of the complexity of the background and difference in intensity levels and class sizes. Previous works are based on bimodal images whereas our work is based on multimodal images.
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