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Image segmentation is an important preprocessing step in a sophisticated and complex image processing algorithm. In segmenting real-world images, the cost of misclassification could depend on the true class. For example, in a two-class (negative or positive class) problem, the cost of misclassifying positive to negative class could not be equal to that of misclassifying negative to positive class. However, existing algorithms do not take into account the unequal misclassification cost. In this letter, motivated by recent advances in machine learning theory, we introduce a procedure to minimize the misclassification cost with class-dependent cost. The procedure assumes the hidden Markov model (HMM) which has been popularly used for image segmentation in recent years. We represent all feasible HMM-based segmenters (or classifiers) as a set of points in the receiver operating characteristic (ROC) space. Then, the optimal segmenter (or classifier) is found by computing the tangential point between the iso-cost line with given slope and the convex hull of the feasible set in the ROC space. We illustrate the procedure by segmenting aerial images with different selection of misclassification costs.