A semisupervised optimization model for determining an efficient segmentation of many input images is proposed in this paper. The advantage of this model is twofold. Firstly, the segmentation is highly controllable as the portion chosen for segmentation can be specified by providing the labeled pixels in images for the model either offline or interactively. Secondly, the model requires only minimal tuning of model parameters during the initial stage. Once initial tuning is done, it can be used to automatically segment a large collection of images that are distinct but share similar features. It is proposed to conduct extensive experiments on various collections of biological images, it will be established that the model proposed is quite computationally efficient and effective for segmentation.