Magnetic resonance (MR) images suffer from intensity inhomogeneity. Segmentation-based approaches can simultaneously achieve both intensity inhomogeneity compensation (IIC) and tissue segmentation for MR images with little noise, but they often fail for images polluted by severe noise. Here, we propose a noise-robust algorithm named noise-suppressed multiplicative intrinsic component optimization (NSMICO) for simultaneous IIC and tissue segmentation. Considering the spatial characteristics in an image, an adaptive nonlocal means filtering term is incorporated into the objective function of NSMICO to decrease image deterioration due to noise. Then, a fuzzy local factor term utilizing the spatial and gray-level relationship among local pixels is embedded into the objective function to reach a balance between noise suppression and detail preservation. Experimental results on synthetic natural and MR images with various levels of intensity inhomogeneity and noise, as well as in vivo clinical MR images, have demonstrated the effectiveness of the NSMICO and its superiority to three competing approaches. The NSMICO could be potentially valuable for MR image IIC and tissue segmentation.