We propose a novel region based segmentation method capable of segmenting objects in presence of significant intensity variation. Current solutions use some form of local processing to tackle intra-region inhomogeneity, which makes such methods susceptible to local minima. In this letter, we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast to existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace using a set of pre-specified basis functions. This representation enables us to accommodate heterogeneous objects, even in presence of noise. We compare our results with three state of the art techniques on a dataset focusing on biological/biomedical images with tubular or filamentous structures. Quantitatively, we achieve a 44% increase in performance, which demonstrates efficacy of the method.