A new technique is presented for enhancing the contrast in digital images, combining the theory of partitioned iterated function system (PIFS) and image segmentation. The image is first segmented through the region growing segmentation technique, and the PIFS enhancement algorithm is applied separately to each image segment. The defined PIFS of each section is modeled by a contractive transformation, which consists of an affine spatial transform, as well as the linear transform of the graylevels of image segment pixels. The transformation of the graylevels is determined by two parameters that adjust the brightness and contrast of the transformed image segment. After the PIFS algorithm is applied to each extracted image segment, a lowpass version of the original image is created. The contrast-enhanced image is obtained by suitably combining the original image with its lowpass version. The proposed regional PIFS approach was applied to numerous test images, ranging from medical data of various modalities to standard images. The obtained quantitative and qualitative results showed superior performance on behalf of the proposed method when compared with three other widely used contrast enhancement methods, namely, contrast stretching, unsharp masking, and contrast-limited adaptive histogram equalization.