An optimized denoised bias correction model with local pre-fitting function for weak boundary image segmentation

Abstract

The active contour model (ACM) plays a paramount part in grasping visual properties of images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to segment images with noise, intensity inhomogeneity or low contrast and consider computation speed for practical applicability. Therefore, an optimized denoised bias correction (ODBC) model incorporating the pre-piecewise fitting function and the variational denoised term into the energy function is proposed for images with low contrast and intensity inhomogeneity. An optimized gradient descent equation and an upgraded regularization term are produced to enhance the robustness and sensitivity of this model. Experiments are conducted to validate that ODBC model possesses the superiority of reliability and speed in segmenting images with inhomogeneous intensity and strengthens the robustness to initial contours. The results manifest that mIOU of ODBC model exceeds 0.9, superior to prime ACMs and deep learning schemes, in segmenting images associated and unrelated with the dataset.