Homogram, or histogram based on homogeneity is employed in our algorithm. Histogram thresholding is a classical and efficient method for the segmentation of various images, especially of CT images. However, MR images are difficultly segmented via this method; as the gray levels of their pixels are too similar to distinguish. The regular histogram of a MR image is usually plain, thus the peaks and valleys of the histogram are hard to find and locate precisely. We proposed a new definition of homogeneity for which a series of sub-images are employed to compute. Therefore, both local and global information are taken in accounted. Then the image is updated with the homogeneity weighted original and average gray levels. The more homogeneous the pixel is, the closer the updated gray level is to the average. The new histogram is calculated based on the updated image. It is much steeper than the regular one. Some indiscernible peaks in the regular histogram can be recognized easily from the new histogram. Therefore a simple but agile peak-finding approach is able to determine objects to segment and corresponding thresholds exactly. Segmentation via thresholding is feasible now even in MR images. Moreover, our algorithm remains speedy even though the accuracy of segmentation advances.