In the last decades, image segmentation has proved its applicability in various areas like satellite image processing, medical image processing and many more. In the present scenario the researchers tries to develop hybrid image segmentation techniques to generates efficient segmentation. Due to the development of the parallel programming, the lattice Boltzmann method (LBM) has attracted much attention as a fast alternative approach for solving partial differential equations. In this paper, first designed an energy functional based on the fuzzy c-means objective function which incorporates the bias field that accounts for the intensity in homogeneity of the real-world image. Using the gradient descent method, corresponding level set equations are obtained from which we deduce a fuzzy external force for the LBM solver based on the model by Zhao. This is a fast, robust method for denoising, independent to the position of the initial contour, effective in the presence of intensity in homogeneity, highly parallelizable and can segment objects with or without edges. assessment on medical and real-world images manifest the performance of the proposed method in terms of speed and efficiency. The work proposed in this paper concentrates on the gray level images.