Textural parameters based on the Spatial Gray Level Dependence Method applied to melanocytic nevi

Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images

Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP(riu4)) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP(riu4) and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM).

Color homogeneity and visual perception of age, health, and attractiveness of female facial skin

BACKGROUND

Evolutionary psychology suggests that skin signals aspects of mate value, yet only limited empirical evidence exists for this assertion.

OBJECTIVES

We sought to study the relationship between perception of skin condition and homogeneity of color/chromophore distribution.

METHODS

Cropped skin cheek images from 170 girls and women (11-76 years) were blind-rated for attractiveness, healthiness, youthfulness, and biological age by 353 participants. These skin images and corresponding melanin/hemoglobin concentration maps were analyzed objectively for homogeneity.

RESULTS

Homogeneity of unprocessed images correlated positively with perceived attractiveness, healthiness, and youthfulness (all r > 0.40; P < .001), but negatively with estimated age (r = -0.45; P < .001). Homogeneity of hemoglobin and melanin maps was positively correlated with that of unprocessed images (r = 0.92, 0.68; P < .001) and negatively correlated with estimated age (r = -0.32, -0.38; P < .001).

LIMITATIONS

Female skin only was studied.

CONCLUSIONS

Skin color homogeneity, driven by melanin and hemoglobin distribution, influences perception of age, attractiveness, health, and youth.

Human (Homo sapiens) facial attractiveness in relation to skin texture and color

The notion that surface texture may provide important information about the geometry of visible surfaces has attracted considerable attention for a long time. The present study shows that skin texture plays a significant role in the judgment of female facial beauty. Following research in clinical dermatology, the authors developed a computer program that implemented an algorithm based on co-occurrence matrices for the analysis of facial skin texture. Homogeneity and contrast features as well as color parameters were extracted out of stimulus faces. Attractiveness ratings of the images made by male participants relate positively to parameters of skin homogeneity. The authors propose that skin texture is a cue to fertility and health. In contrast to some previous studies, the authors found that dark skin, not light skin, was rated as most attractive.

Markov optical texture parameters as prognostic indicators in ovarian carcinoma

Texture is a descriptive property of a surface describing the morphometric heterogeneity of complex structures. Computer aided image analysis allows optical texture measurement and analysis of gray-scale images. The authors, utilizing image analysis, prospectively studied Markov nuclear texture features to determine their relevance as prognostic indicators of survival in patients with epithelial ovarian carcinoma. Ninety-nine consecutive patients with ovarian cancer, treated initially with surgery were evaluated for their length of survival, level of cytoreduction, FIGO stage, grade, histology, and DNA index, as well as 20 Markov texture features. Markov nuclear texture features were quantified using image analysis. Mean follow-up for the study population was 64 months (median 59) with a range from 51 to 89 months. Five optical texture features showed significant correlation with length of survival. Difference entropy (P = 0.033) and information measure A (P = 0.041) were both indirectly correlated with survival while information measure B (P = 0.030), correlation coefficient (P = 0.045), and the maximum correlation coefficient (P = 0.041) were directly correlated. Only sum entropy (P = 0.035), FIGO stage (P = 0.0031), and level of cytoreduction (P < 0.0001) were independent predictors of survival in this population. Optical texture can be quantified by image analysis. Utilizing multivariate analysis, the Markov texture feature, sum entropy, was demonstrated to be an independent prognostic indicator of survival in patients with epithelial ovarian cancer. FIGO stage and optimal cytoreduction also were independent prognostic indicators of survival.