 We have presented a multilevel segmentation of histopathological images.  It has two main contributions, first it introduces a new set of high level texture features of spatial organization in tissue components.  The second ,it proposes to obtain multiple segmentations of a graph constructed on the tissue objects and combine them by an ensemble function.  Finally multilevel scheme will increase the diversity of individual segmentations.  First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship.  As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images.  Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function.  This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result.