Lesion detection using Gabor-based saliency field mapping

机译：使用基于Gabor的显着性字段映射进行病变检测

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In this paper, we present a method that detects lesions in two-dimensional (2D) cross-sectional brain images. By calculating the major and minor axes of the brain, we calculate an estimate of the background, without any a priori information, to use in inverse filtering. Shape saliency computed by a Gabor filter bank is used to further refine the results of the inverse filtering. The proposed algorithm was tested on different images of " The Whole Brain Atlas" database. The experimental results have produced 93% classification accuracy in processing 100 arbitrary images, representing different kinds of brain lesion.

机译：在本文中，我们提出了一种在二维（2D）横截面脑图像中检测病变的方法。通过计算大脑的长轴和短轴，我们可以计算出背景估计值，而无需任何先验信息，即可用于逆滤波。由Gabor滤波器组计算的形状显着性用于进一步优化逆滤波的结果。该算法在“全脑图谱”数据库的不同图像上进行了测试。实验结果在处理100张任意图像时产生93％的分类精度，代表了不同种类的脑部病变。

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来源
《Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3》|2007年|65123E.1-65123E.8|共8页
会议地点 San DiegoCA(US)
作者
Marc Macenko; Rutao Luo; Mehmet Celenk; Limin Ma; Qiang Zhou;
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作者单位

School of Electrical Engineering and Computer Science, Ohio Univ., Athens, OH 45701, USA;

Chrontel Inc., San Jose, CA 95131;

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原文格式 PDF
正文语种 eng
中图分类医用物理学;光、激光生物医学;
关键词
brain lesion; background estimation; gabor-filter;

机译：脑部病变背景估计；伽柏过滤器;

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Lesion detection using Gabor-based saliency field mapping

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