Detection of breast cancer in form of masses at initial stage becomes difficult because of obscured nature of mammograms by surrounding tissues. This poor visibility of masses addresses to the necessity of accurate contrast enhancement method. This study introduces a novel kernel-based fuzzy clustering approach to enhance the contrast of masses. Novelty of the proposed technique is incorporation of two important features of mammograms that convey the properties of masses. Local entropy and intensity mean of each kernel position play the key role to enhance masses. A kernel is moved across the mammograms to collect all possible values of those features, and hence, they are exploited as the input data of fuzzy clustering technique. Performance of the proposed approach is compared with two other conventional contrast enhancement techniques. Both subjective and objective evaluations of the proposed technique show an evidence of improvement in compare to other methods. Moreover, unwanted enhancement of obscured tissues is also suppressed in the proposed approach. © 2018, IGI Global. All rights reserved.