Bias field is a smooth intensity variation, which emanate during the process of image procurement. Bias removal is an essential prerequisite while incorporating computer assisted diagnosis. Several bias correction algorithms are proposed till date. This paper scrutinizes the prominent bias correction algorithms, LEMS, MICO, BCFCM and N3. These investigations carried over the substantial amount of T1 and T2 weighted brain MR images with different bias spectrum from Brain Web website. Algorithms efficiency are analyzed in spectral wise, slice wise, and type wise. Based upon the performance indicators coefficient of variation and coefficient of joint variation, the algorithms are assessed and ranked. The result concludes that which algorithm exterminates the bias field, presents in brain MR images in an efficient manner.

中文翻译：

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颅脑MR图像偏差校正技术的性能分析。

偏置场是一个平滑的强度变化，在图像获取过程中产生。消除偏差是合并计算机辅助诊断的必要先决条件。迄今为止提出了几种偏差校正算法。本文详细研究了著名的偏差校正算法LEMS，MICO，BCFCM和N3。这些研究对来自Brain Web网站的大量具有不同偏差谱的T1和T2加权脑MR图像进行了研究。在频谱方面，片段方面和类型方面分析算法效率。基于性能指标的变异系数和联合变异系数，对算法进行评估和排名。结果得出结论，哪种算法可以消除偏场，并以有效的方式呈现在大脑MR图像中。