Given the interference from noise

it is difficult to segment noisy images by traditional image segmentation method. Therefore

we propose a new neutrosophic image segmentation method integrating LPG&PCA(principal component analysis with local pixel grouping). First

the input image is converted into neutrosophic images by using the neutrosophic set theory. Then

we filter the noise image by applying the proposed -LPG&PCA filtering operation

and advance the denoised image by employing the -enhancing operation

with the uncertainty information from the input image

to smooth the noisy points in the input image. Finally

we segment the denoised and enhanced image with a -means clustering method. The experimental results indicate that the proposed algorithm can eliminate noise effectively as well as improve the PSNR

therefore

our method can achieve desirable results in noise immunity and segmentation accuracy. The accuracy of our algorithm is improved

through the neutrosophic set theory and the uncertainty information from the input image. Theoretical analysis and experimental results show the effectiveness of the proposed algorithm.