A novel automatic tongue image segmentation algorithm: Color enhancement method based on L*a*b* color space

Chen, Li; Wang, Dongyi; Liu, Yiqin; Gao, Xiaohang; Shang, Huiliang

doi:10.1109/bibm.2015.7359818

Cited by 16 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the perspective of exploiting low-level features, a direct idea is to utilize the color features to assist segmentation. [14][15][16] For example, Zhongxu et al 15 Although research work on unsupervised methods has achieved sound progress in the absence of labor-intensive image annotations, there are important limitations of these methods, for example, the inefficiency of learning to adapt to the appearance variations of individual images due to their iterative procedures or the matrix decomposition step involved. In particular, the accuracy of these methods deteriorates quickly when cluttered backgrounds are present.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

A robust interclass and intraclass loss function for deep learning based tongue segmentation

Cai

Wang

Liu

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

The fast and robust segmentation of tongue images is a prerequisite to achieve automatic tongue diagnosis in traditional Chinese medicine. In order to assist tongue diagnosis in real-life scenarios, an ideal tongue segmentation method would need to obtain the entire tongue body as well as its precise contours. However, the similar appearance among the tongue body, the coating, and the lips hinders the performance for most unsupervised learning methods that primarily utilize low-level visual features. On the other hand, although the supervised deep convolutional neural networks (DCNNs) that typically depend on the widely used crossentropy loss can achieve better accuracy, they are very prone to segment image to multiple trivially areas. To address both of the above issues, we make an attempt to boost the segmentation performance of DCNNs with a novel auxiliary loss function that seeks to exploit large margin learning for end-to-end tongue segmentation models. Specifically, we first propose a loss function that involves interclass and intraclass costs to directly measure the distance among pixels that belong to different connected regions. Then, we explore the potential ability of this loss function as a regularization for different segmentation networks such as those with attention modules and the deeply supervised network. Finally, a theoretical analysis for this learning scheme is presented. Through experiments on challenging datasets, we show that the proposed approach can be easily integrated into state-of-the-art networks to boost their performance at the tongue segmentation task without bells and whistles.

show abstract

Section: Unsupervised Methodsmentioning

confidence: 99%

A robust interclass and intraclass loss function for deep learning based tongue segmentation

Cai

Wang

Liu

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Though the work showed good accuracy and less complexity, it needed four points to be specified for initial contour formation. Chen et al 61 performed tongue image segmentation utilizing the same dataset via MATLAB and the test result showed 98.4% accuracy taking 11.46 seconds to compute with C2G2FSnake. Testing on 40 images, recent work has been reported to achieve 75% accuracy 80…”

Section: Image Segmentation and Feature Extractionmentioning

confidence: 99%

“…However, the performance depends on correct choice of markers to avoid oversegmentation. The watershed transform is also utilized as a segmentation technique for tongue diagnosis 50, 61, 81…”

Section: Image Segmentation and Feature Extractionmentioning

confidence: 99%

Advances in automated tongue diagnosis techniques

Tania

Lwin

2019

Integrative Medicine Research

View full text Add to dashboard Cite

Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work reviews the recent advances in tongue diagnosis, which is a significant constituent of traditional oriental medicinal technology, and explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system.

show abstract

“…In addition, researchers have transformed tongue images into different color spaces for feature analysis. Li [18] first extracted the color features of tongue images based on the HSV color space, used an enhancement method, and then used the brightness feature in the Lab space to extract the tongue contour. In [19], the statistical characteristics of the tongue color were analyzed, and the typical tongue color in the RGB color space and the value in the Lab color space was determined.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of the Traditional Chinese Medicine Constitution System Based on the Diagnosis of Tongue and Consultation

Yuan

Liao

2021

IEEE Access

View full text Add to dashboard Cite

A novel automatic tongue image segmentation algorithm: Color enhancement method based on Lab* color space

Cited by 16 publications

References 3 publications

A robust interclass and intraclass loss function for deep learning based tongue segmentation

A robust interclass and intraclass loss function for deep learning based tongue segmentation

Advances in automated tongue diagnosis techniques

Design and Implementation of the Traditional Chinese Medicine Constitution System Based on the Diagnosis of Tongue and Consultation

Contact Info

Product

Resources

About