Breast lesion segmentation in magnetic resonance (MR) images is one of the most 
important parts of clinical diagnostic tools. Pixel classification methods have 
been frequently used in image segmentation with two supervised and unsupervised 
approaches up to now. Supervised segmentation methods lead to high accuracy, but 
they need a large amount of labeled data, which is hard, expensive, and slow to 
be obtained. On the other hand, unsupervised segmentation methods need no prior 
knowledge and lead to low performance. However, semi-supervised learning which 
uses not only a few labeled data, but also a large amount of unlabeled data 
promises higher accuracy with less effort. In this paper, we propose a new 
interactive semi-supervised approach to segmentation of suspicious lesions in 
breast MRI. Using a suitable classifier in this approach has an important role 
in its performance; in this paper, we present a semi-supervised algorithm 
improved self-training (IMPST) which is an improved version of self-training 
method and increase segmentation accuracy. Experimental results show that 
performance of segmentation in this approach is higher than supervised and 
unsupervised methods such as K nearest neighbors, Bayesian, Support Vector 
Machine, and Fuzzy c-Means.