It was difficult for the conventional HE methods to give consideration to restraining noise while emphasizing features. A novel image enhancement technique based on image segment by clustering and histogram equalization was proposed to overcome the drawback. The input image was smoothed by a Gaussian filter, and the fuzzy C-means clustering algorithm was used to segment the low-pass image into several partitions, and then the edge information and morphological operations were used to fix the segmentation result. Each partition was assigned to a new dynamic range according to its mean and standard deviation. Based on the dynamic range, the histogram equalization process was applied independently to these partitions. Smooth filtering was applied to eliminate the blocking-effect. The results of experiments showed that been processed by the proposed algorithm, the contrast was significantly enhanced and noise was effectively suppressed. The proposed algorithm not only can be used for image enhancement, but also can be used for dynamic range compression, so it has broad application prospects.