An improved segmenter has been developed which partitions a monochrome image into homogeneous regions using local neighborhood operations. Perkin's well-known edge-based segmenting algorithm  is used to partition those portions of an image with little detail (low edge density) into regions of uniform intensity. A technique is introduced which segments the remainder of the image to reveal details that were previously lost. Region merging is then performed by removing selected boundary pixels that separate sufficiently similar (e.g., in average intensity) regions subject to the constraint that the boundary pixel quality (e.g. edge strength) is below a selected threshold. Region merging is repeated using less and less restrictive merging criteria until the desired degree of segmentation (e.g. number of regions) is obtained.