Patch-Driven Tongue Image Segmentation Using Sparse Representation

Weixia Liu,Changen Zhou,Zhongyi Hu,Zuoyong Li

doi:10.1109/access.2020.2976826

Abstract

Tongue diagnosis plays a key role in TCM (Traditional Chinese Medicine) diagnosis. Tongue image segmentation lays a solid foundation for quantitative tongue analysis and diagnosis. However, the segmentation of tongue body is challenging due to the factors such as large personal variation of tongue body on color, texture and shape, as well as weak edges caused by similar color between tongue body and neighboring tissues, especially the lip. Existing segmentation methods usually use only single color component and simple prior knowledge, thus leading to inaccuracy and instability. To alleviate these issues, a patch-driven segmentation method with sparse representation is proposed in this paper. Specifically, each patch in the testing image is sparsely represented by patches in the spatially varying dictionary, which is constructed by the local patches of training images. The derived sparse coefficients are then employed to estimate the tongue probability. Finally, the hard segmentation is obtained by applying the maximum a posteriori (MAP) rule on the tongue probability map and further polished with morphological operations. The proposed method has been extensively evaluated on a tongue image dataset including 290 subjects using 10-fold cross-validation, as well as additional 10 unseen testing subjects. The proposed method has achieved more accurate segmentation results, compared with the state-of-the-art methods.

Highlights

Tongue diagnosis [1], [2] aims to draw physiological and pathological cues according to tongue body’s features including color, texture, shape, coating and so on
(2) We develop a novel color information based criterion to evaluate the similarity between a testing patch and a training patch, and use it to pre-select training patches for reducing the size of the dictionary in sparse representation and saving the computational time
Segmentation results on 8 representative tongue images are first used to perform qualitative comparison between the proposed method and state-of-the-art methods, i.e., gradient vector flow (GVF)-region merging (RM) [15] and a popular patch-based label fusion method called nonlocal-means [17]

Summary

INTRODUCTION

Tongue diagnosis [1], [2] aims to draw physiological and pathological cues according to tongue body’s features including color, texture, shape, coating and so on. Coupé et al [17] presented a patch-based label fusion method termed as the nonlocal-means method for segmentation of hippocampus and ventricle. Motivated by the nonlocal-means method [17] and the sparse representation techniques, we propose a novel patch-based sparse representation method for tongue image segmentation in this paper. 2) The nonlocal-means method took the weighted average of training pixels’ labels as the probability of the testing pixel belonging to the object (tongue body), where the weight of a training pixel is defined as the similarity between its corresponding training patch and the testing patch. Different from the nonlocal-means method, the proposed method uses sparse coefficients obtained by sparse representation to calculate the probability of the testing pixel belonging to the object.

RELATED WORK

SPARSE REPRESENTATION

EXPERIMENTAL RESULTS

CONCLUSION