We propose a general-purposed parallel algorithm for volume segmentation, which does not require any prior knowledge on volume nor region. The algorithm provides binary tree structured split-and-merge mechanism to search and localize boundaries along discontinuities and adapts the partition of volume to those detected discontinuities. This algorithm is independent from order of processing or seed selection. And, even though overlapping only one voxel wide boundary between process blocks, by adopting the smoothness-based local feature as homogeneity criteria, consistencies are maintained without overhead of communication between adjacent process blocks. Our efficient hierarchical step-wised mechanism in merging target evaluation makes merge process so simple and efficient that only two brother blocks are considered at each merge step in binary fashion. Experimental results on an artificial and a CT scan volume data are shown.