Modified SLIC segmentation for medical hyperspectral cell images

Abstract

Simple linear iterative clustering (SLIC) is a fast and effective method for superpixel segmentation. However, the similarity measurement method of typical SLIC based on spatial and spectral features fails to get precise segmentation boundaries, especially for the images with complex and irregular shapes. To address this issue, a modified SLIC (MSLIC) method based on spectral, color, and texture information is proposed for medical hyperspectral cell images. The Gabor filter is used to exploit detailed texture features, which processes the image by using signal Fourier transform in the frequency domain. The MSLIC employs normalization, Gamma correction, and principal component analysis (PCA) to preprocess medical hyperspectral images, in which the texture features are integrated with spectral and spatial features to measure the distance. The under-segmentation error and boundary recall are used as the criterion of segmentation. Experiments for two medical datasets indicate that MSLIC achieves better segmentation performance than the typical SLIC method.