Fingerprint segmentation is an important step in an automatic fingerprint recognition system. Due to applications of various sensors, fingerprint segmentation inevitably suffers from sensor interoperability problem. K-means algorithm is one solution to address the sensor interoperability problem in fingerprint segmentation. However, the traditional k-means based method does not well deal with the border between the foreground and the background. The over-segmentation of foreground area may appear and some important minutiae are lost. To effectively address the issue, we propose an improved k-means based segmentation method with sensor interoperability called ISKI. ISKI performs the secondary determination to the blocks which have similar distances with the two cluster centers after k-means clustering. The proposed method is applied on a number of fingerprint databases which are collected by various sensors. Experimental results show our proposed method significantly improves the accuracy of segmentation.