Xingzheng Wang

An Optimized Tongue Image Color Correction Scheme

The color images produced by digital cameras are usually device-dependent, i.e., the generated color information (usually presented in RGB color space) is dependent on the imaging characteristics of specific cameras. This is a serious problem in computer-aided tongue image analysis because it relies on the accurate rendering of color information. In this paper, we propose an optimized correction scheme that corrects the tongue images captured in different device-dependent color spaces to the target device-independent color space. The correction algorithm in this scheme is generated by comparing several popular correction algorithms, i.e., polynomial-based regression, ridge regression, support vector regression, and neural network mapping algorithms. We test the performance of the proposed scheme by computing the CIE L*a*b* color difference (ΔE*ab) between estimated values and the target reference values. The experimental results on the colorchecker show that the color difference is less than 5 (ΔE*ab <; 5), while the experimental results on real tongue images show that the distorted tongue images (captured in various device-dependent color spaces) become more consistent with each other. In fact, the average color difference among them is greatly reduced by more than 95%.

Statistical Analysis of Tongue Images for Feature Extraction and Diagnostics

In this paper, an in-depth analysis on the statistical distribution characteristics of human tongue color that aims to propose a mathematically described tongue color space for diagnostic feature extraction is presented. Three characteristics of tongue color space, i.e., tongue color gamut that defines the range of colors, color centers of 12 tongue color categories, and color distribution of typical image features in the tongue color gamut, are elaborately investigated in this paper. Based on a large database, which contains over 9000 tongue images collected by a specially designed noncontact colorimetric imaging system using a digital camera, the tongue color gamut is established in the CIE chromaticity diagram by an innovatively proposed color gamut boundary descriptor using one-class SVM algorithm. Thereafter, centers of 12 tongue color categories are defined accordingly. Furthermore, color distributions of several typical tongue features, such as red points and petechial points, are obtained to build a relationship between the tongue color space and color distributions of various tongue features. With the obtained tongue color space, a new color feature extraction method is proposed for diagnostic classification purposes, with experimental results validating its effectiveness.

Robust Texture Image Representation by Scale Selective Local Binary Patterns

Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition applications, such as texture recognition. It could effectively address grayscale and rotation variation. However, it failed to get desirable performance for texture classification with scale transformation. In this paper, a new method based on dominant LBP in scale space is proposed to address scale variation for texture classification. First, a scale space of a texture image is derived by a Gaussian filter. Then, a histogram of pre-learned dominant LBPs is built for each image in the scale space. Finally, for each pattern, the maximal frequency among different scales is considered as the scale invariant feature. Extensive experiments on five public texture databases (University of Illinois at UrbanaChampaign, Columbia Utrecht Database, Kungliga Tekniska Högskolan-Textures under varying Illumination, Pose and Scale, University of Maryland, and Amsterdam Library of Textures) validate the efficiency of the proposed feature extraction scheme. Coupled with the nearest subspace classifier, the proposed method could yield competitive results, which are 99.36%, 99.51%, 99.39%, 99.46%, and 99.71% for UIUC, CUReT, KTH-TIPS, UMD, and ALOT, respectively. Meanwhile, the proposed method inherits simple and efficient merits of LBP, for example, it could extract scale-robust feature for a 200 × 200 image within 0.24 s, which is applicable for many real-time applications.

An IGA-based design support system for realistic and practical fashion designs

In this paper, a customised fashion design system is proposed for non-professional users (general customers) to create their preferred fashion designs in a user-friendly way. The proposed sketch design system consists of a sketch representation and composing method, an interactive genetic algorithm (IGA)-based design model, and a user-friendly interface. The sketch representation and composing method generates feasible design sketches, based on the design parameters defined by the IGA-based design model, and the sketches are presented to customers via the user-friendly interface. Experimental results have demonstrated that the proposed system is effective in generating fashion design sketches reflecting users preference.

A high quality color imaging system for computerized tongue image analysis

In order to improve the quality and consistency of tongue images acquired by current imaging devices, this research aims to develop a novel imaging system which records human tongue information faithfully and precisely for medical analysis. A thorough demand analysis is firstly conducted to summarize requirements for reliable rendering of all possible medical clues, i.e., color, texture and geometric features. Then a series of system design criteria are illustrated accordingly, and by following them, three hardware modules of the imaging system, including illuminant, lighting path and imaging camera, are optimally proposed. Moreover, one built-in software module, the color correction process, is also provided to compensate color variations caused by system components. Finally, several important performance indicators, including illumination uniformity, system reproducibility and accuracy, are elaborately tested. Experimental results show that captured images are in high quality and keep stable when acquisitions are repeated. The largest color difference between any two acquired images is 1.6532, which is hardly to be distinguished by human observation. Compared to existing devices, the proposed system could provide much more accurate and stable solution for tongue image acquisition. Besides, this developed imaging system has been evaluated by doctors of Traditional Chinese Medicine for almost three years and over 9,000 tongue images have been collected, analysis results based these data also validate the effectiveness of the proposed system.

Solving the two-dimensional irregular objects allocation problems by using a two-stage packing approach

Packing problems are combinatorial optimization problems that concern the allocation of multiple objects in a large containment region without overlap and exist almost everywhere in real world. Irregular objects packing problems are more complex than regular ones. In this study, a methodology that hybridizes a two-stage packing approach based on grid approximation with an integer representation based genetic algorithm (GA) is proposed to obtain an efficient allocation of irregular objects in a stock sheet of infinite length and fixed width without overlap. The effectiveness of the proposed methodology is validated by the experiments in the apparel industry, and the results demonstrate that the proposed method outperforms the commonly used bottom-left (BL) placement strategy in combination with random search (RS).

A New Tongue Colorchecker Design by Space Representation for Precise Correction

In order to improve the correction accuracy on tongue colors by use of a Munsell colorchecker, this research aims to design a new colorchecker by aid of tongue color space. Three essential issues leading to the development of this space-based colorchecker are elaborately investigated in this study. First, based on a large and comprehensive tongue database, tongue color space is established by which all visible colors can be classified as tongue or nontongue colors. Hence, colors of the designed tongue colorchecker are selected from tongue colors to achieve high correction performance. Second, the minimum sufficient number of colors involved in a colorchecker is yielded by comparing the correction accuracy when different number (ranged from 10 to 200) of colors are contained. Thereby, 24 colors are included because the obtained minimum number of colors is 20. Finally, criteria for optimal color selection and its corresponding objective function are presented. Two color selection methods, i.e., greedy and clustering-based selection method, are proposed to solve the objective function. Experimental results show that clustering-based one outperforms its counterpart to generate the new tongue colorchecker. Compared to a Munsell colorchecker, this proposed space-based colorchecker can greatly improve the correction accuracy by 48%. Further experimental results on more correction task also validate its effectiveness and superiority.

Facial image medical analysis system using quantitative chromatic feature

In order to investigate whether the appearance of a human face can be utilized for diagnostic purposes, which have been practiced for thousands of years in Traditional Chinese Medicine (TCM), this paper aims to present a computerized facial image analysis system by using quantitative chromatic features for disease diagnosis applications. A face image acquisition device is dedicatedly designed to acquire image samples from volunteers who have three types of health conditions: normal health, icterohepatitis, and severe hepatitis. Then, after color calibration on the acquired images to remove noises caused by lighting fluctuations, quantitative dominant color features are extracted by fuzzy clustering method. In order to further improve the diagnosis accuracy, a feature selection procedure is involved to identify the most discriminative feature subset for the diagnostic classification. Lastly, based on these selected quantitative feature, each face image could be diagnosed into different health groups. Experiments are conducted based on a database which includes over 300 sample images, and the result shows that the overall diagnosis accuracy between healthy samples and other two diseases is higher than 88%. Hence the feasibility of disease diagnosis by inspecting the chromatic feature of human face could be verified.

Tongue Color Analysis for Medical Application

An in-depth systematic tongue color analysis system for medical applications is proposed. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut. The ratio of each color for the entire image is calculated and forms a tongue color feature vector. Experimenting on a large dataset consisting of 143 Healthy and 902 Disease (13 groups of more than 10 samples and one miscellaneous group), a given tongue sample can be classified into one of these two classes with an average accuracy of 91.99%. Further testing showed that Disease samples can be split into three clusters, and within each cluster most if not all the illnesses are distinguished from one another. In total 11 illnesses have a classification rate greater than 70%. This demonstrates a relationship between the state of the human body and its tongue color.

Computerized facial diagnosis using both color and texture features

Facial diagnosis is an important diagnostic tool, and has been practiced by various traditional medicines for thousands of years. However, due to its qualitative and subjective nature, it cannot be accepted in mainstream medicine. To circumvent these issues, computerized facial diagnosis using color and texture features are extracted from facial blocks representing a facial image. A facial color gamut is constructed and six centroids located to help calculate the facial color feature vector. As for the texture feature, a 2-dimensional Gabor filter with various scales and orientations are applied. Both features are combined to diagnosis the face. The experimental results were carried out on a large dataset consisting of 142 Health and 1038 Disease samples. Using both extracted features facial gloss was first detected and employed to distinguish Health and Disease samples with an average accuracy of 99.83%. Illnesses in Disease were also separated by the analysis of each facial block. The best result was achieved using all facial blocks, which successfully classified (>71%) six illnesses.