Histogram Thresholding in Image Segmentation: A Joint Level Set Method and Lattice Boltzmann Method Based Approach

The level set method (LSM) has been widely utilized in image segmentation due to its intrinsic nature which sanctions to handle intricate shapes and topological changes facilely. The current work proposed an incipient level set algorithm, which uses histogram analysis in order to efficiently segmenting images. The computational intricacy of the proposed LSM is greatly reduced by utilizing the highly parallelizable lattice Boltzmann method (LBM). The incipient algorithm is efficacious and highly parallelizable. Recently, with the development of high dimensional astronomically an immense-scale images contrivance, the desideratum of expeditious and precise segmentation methods is incrementing. The present work suggested a histogram analysis based level set approach for image segmentation. Experimental results on real images demonstrated the performance of the proposed method. It is established that the proposed segmentation methods using Level set methods for image segmentation achieved 0.92 average similarity value and average 1.35 s to run the algorithm, which outperformed Li method for segmentation.

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