The complexity and high pixel count of particle contours often lead to issues with current minimum circumscribing circle (MCC) and maximum inscribed circle (MIC) algorithms, such as non-convergence or local optima, due to improper selection of search points. A partial area search (PAS)-based optimization algorithm for MCC and MIC was proposed to address these issues. Euclidean distance transformation (EDT) was employed by the algorithm to ascertain a center point, around which distinct local search areas were delineated. Then the candidate points required for MCC and MIC were searched in the local region respectively. Finally, the required circles were obtained by calculation. For MCC calculation, a detailed method of direct determination via two points was presented, prioritizing points on the outermost periphery of the local area for initial circle calculation. This approach allowed for certain particles to achieve MCC without iteration, mitigating errors due to improper search point selection and reducing the need for subsequent iterative computations. MIC calculation was commenced by searching for candidate points within the local area, followed by the computation of MIC using a Voronoi diagram, thereby avoiding iterative steps and enhancing calculation precision and efficiency. After MCC and MIC determination, particle irregularity could be computed. Through comparative analysis of existing datasets and experimental data of actual particles, the stability and accuracy of the optimization algorithm have been proven, and it has high computational efficiency, while being suitable for low resolution particle images. The research results provide an effective optimization algorithm for the analysis of particle morphology.