A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set

Abstract

Image segmentation is an important step in
image processing and analysis, pattern recognition, and
machine vision. A few of algorithms based on level set
have been proposed for image segmentation in the last
twenty years. However, these methods are time
consuming, and sometime fail to extract the correct
regions especially for noisy images. Recently,
neutrosophic set (NS) theory has been applied to image
processing for noisy images with indeterminant
information. In this paper, a novel image segmentation
approach is proposed based on the filter in NS and level
set theory. At first, the image is transformed into NS
domain, which is described by three membership sets (T,
I and F). Then, a filter is newly defined and employed to
reduce the indeterminacy of the image. Finally, a level
set algorithm is used in the image after filtering operation
for image segmentation. Experiments have been
conducted using different images. The results
demonstrate that the proposed method can segment the
images effectively and accurately. It is especially able to
remove the noise effect and extract the correct regions on
both the noise-free images and the images with different
levels of noise.