This paper presents and develops an automated algorithm for segmenting speculated masses of the mammogram images based on pulse coupled neural networks (PCNN) in conjunction with fuzzy set theory. Mammogram image segmentation has proven to be a difficult task due to the low contrast between normal and malignant glandular tissues and the noise in such images that makes it very difficult to segment them. Therefore, the fuzzy histogram hyperbolization (FHH) algorithm is first used as a filter before the segmentation process. Then, the PCNN is applied to segment the images to arrive at the final result. To test the effectiveness of PCNNs on high quality images, a set of mammogram images was chosen. The experimental results show that the proposed algorithm performs well as compared to the fuzzy thresholds and fuzzy C-mean results.