Early detection of breast cancer by means of thermal imaging has a long and extremely controversial history. Recently, the availability of highly sensitive infrared (IR) cameras which can produce high-resolution diagnostic images of the temperature and vascular changes of breasts, as well as a better knowledge of advanced image processing techniques, has generated a renewed interest. The objective of this study is to investigate fractal analysis of breast thermal images and to develop an algorithm for detecting benignity and malignancy of breast diseases. The study is based on IR images captured by thermal camera, in which the resolution of the results is within the state of the art of IR camera. A total of 7 malignant cases and 8 benign cases have been considered. The breast images were first segmented by fuzzy c-means clustering. Then the first hottest regions for each image were identified and the fractal dimension of those regions was computed. It is shown that the fractal dimension results significantly differ between malignant and benign patterns, suggesting that fractal analysis may potentially improve the reliability of thermography in breast tumor detection.