Tongue diagnosis plays an essential role in diagnosing the syndrome types, pathological types, lesion location and clinical stages of cancers in Traditional Chinese Medicine (TCM). The quality of the tongue image datasets is crucial to tongue image segmentation in modern tongue diagnosis. However, the tongue image dataset maintains a challenging problem because of the lack of datasets for the sublingual image, the complexity and scarcity of the tongue images. In this paper, we propose an effective segmentation framework for tongue images, called Moment Invariants with Data Augmentation (DAMI), which can be flexibly applied to different segmentation models. To overcome over-fitting caused by excessive data augmentation, a data augmentation module with random transformations is designed to achieve appropriate data augmentation. Meanwhile, we develop a new moment invariants module to optimize data augmentation in image segmentation. In addition, a novel tongue image dataset, Lingual-Sublingual Image Dataset (LSID), has been established for the classification and segmentation of tongue or sublingual veins. Experimental results confirm that the models using DAMI can remarkably outperform the existing methods on LID, SID and BioHit for the tongue image segmentation.

Variational Bayesian approaches have been successfully applied to image segmentation. They usually rely on a Potts model for the hidden label variables and a Gaussian assumption on pixel intensities within a given class. Such models may however be limited, especially in the case of multicomponent images. We overcome this limitation with HOGMep, a Bayesian formulation based on a higher-order graphical model (HOGM) on labels and a Multivariate Exponential Power (MEP) prior for intensities in a class. Then, we develop an efficient statistical estimation method to solve the associated problem. Its flexibility accommodates to a broad range of applications, demonstrated on multicomponent image segmentation and restoration.

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Variational Bayesian approximations have been widely used in fully Bayesian inference for approximating an intractable posterior distribution by a separable one. Nevertheless, the classical variational Bayesian approximation (VBA) method suffers from slow convergence to the approximate solution when tackling large dimensional problems. To address this problem, we propose in this paper a more efficient VBA method. Actually, variational Bayesian issue can be seen as a functional optimization problem. The proposed method is based on the adaptation of subspace optimization methods in Hilbert spaces to the involved function space, in order to solve this optimization problem in an iterative way. The aim is to determine an optimal direction at each iteration in order to get a more efficient method. We highlight the efficiency of our new VBA method and demonstrate its application to image processing by considering an ill-posed linear inverse problem using a total variation prior. Comparisons with state of the art variational Bayesian methods through a numerical example show a notable improvement in computation time.

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This paper introduces two unsupervised approaches for large dimensional ill-posed inverse problems. These approaches are based on improved variational Bayesian (VB) methodologies, where a functional optimization problem is involved. We propose to solve this problem by adapting the subspace optimization methods into the functional space. The application of these approaches to image processing problems is considered thanks to a TV prior. We highlight the efficiency of our approaches through comparisons with a classical VB based one on a super-resolution problem.

Metal and beam-hardening artifacts are tough issues in Computed Tomography (CT) images. This paper proposes an iterative Maximum A Posteriori (MAP) reconstruction algorithm aiming to reduce both of them. This algorithm is based on a multi-energy acquisition system, a Gaussian noised measuring model and a basis material decomposition formula. In the Multi-Energy Computed Tomography (MECT) system, an energy discriminant detector which can measure the flux of photons at different energies is supposed to be employed. Our method can reconstruct two separate base material density maps where the metal and beam-hardening artifacts are highly reduced.