Biomedical image processing that offers computer-aided diagnosis is much more popular due to the availability of high quality and large quantity of medical data. Our well-developed biomedical image computing system, which automatically extracts and segments the nucleus and cytoplasm of cell in medical images, is no doubt following this idea. Nonetheless, even though previous system provide good algorithmic performance, its throughput is limited by high computation load and data dependency. Therefore, we deploy spectral graph partitioning to improve computation speed of the most complex module, maker-controlled watershed transform for nuclei detection. By modeling our problem as a graph and embedding architectural costs as the attributes in vertices and edges, we equally distribute workload among processors and reduce overhead in data transfer rate. We deploy the proposed approach on Intel Core i7-930 CPU with four cores and eight threads and test 153 medical images; as a consequence, we achieve less data transfer and better load balance as compared to conventional workload distribution through clustering and other graph partitioning methods.