Image segmentation using CUDA accelerated non-local means denoising and bias correction embedded fuzzy c-means (BCEFCM)

• A framework is proposed for segmenting images with noise and bias field.
• The noise is first removed by a CUDA accelerated non-local means denoising method.
• The noise removed image is then segmented by bias correction embedded FCM.
• The framework is able to segment images with noise and correct the bias field.
• The framework is faster and more accurate than state-of-the-art methods.

Due to intensity overlaps between interested objects caused by noise and intensity inhomogeneity, image segmentation is still an open problem. In this paper, we propose a framework to segment images in the well-known image model in which intensities of the observed image are viewed as a product of the true image and the bias field. In the proposed framework, a CUDA accelerated non-local means denoising method is first used to remove noise from the image. Then, a bias correction embedded fuzzy c-means (BCEFCM) method is proposed to segment the image and correct the bias field simultaneously. To ensure the slowly and smoothly varying property of the bias field, we convolve it with a normalized kernel as soon as it is updated in each iteration. The proposed framework has been extensively tested on both selected synthetic and real images and public BrainWeb and IBSR datasets. Experimental results and comparison analysis demonstrate that the proposed framework is not only able to deal with noise and correct the bias field but it is also faster and more accurate than state-of-the-art methods.

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