BackgroundThe measurement of dynamic parameters, such as the length of applanation and the amplitude of deformation, is significant for evaluating corneal properties. Most of the corneal properties (related to shape) including the anterior corneal curvature and the thickness of cornea can be easily measured using some existing techniques. However, they only provide the static or pseudo-dynamic analysis. Based on Corvis ST images, the dynamic features after corneal boundaries detection and parameter estimation will be helpful for corneal analysis.MaterialThe study included 40 eyes in normal group (ranging from 19 to 45 years old) and 30 eyes in keratoconus group (ranging from 16 to 40 years old). These eyes were examined by Corvis ST and for each one a sequence of 140 images was obtained. Besides, 11 subjects of each group were also tested by Pentacam.MethodsBy analyzing the video from the Corvis ST imaging, the fully dynamic curvature topography is proposed to evaluate the response of the anterior corneal surface to the air puff. The new method not only quantitatively measures the intact variation of anterior corneal surface but also provides an intuitive way to observe the dynamic change of the anterior corneal surface in the whole air stream process. The proposed method consists of three main steps: cornea segmentation, curvature estimation and integrated visualization. An automatic segmentation method based on the combination of prior knowledge with phase symmetry and asymmetry theory is firstly presented to detect the corneal boundaries. The Landau-new method is then used to estimate the anterior corneal surface. The corneal dynamic topography is finally obtained by combining the dynamic parameters with the original Corvis ST video, which is an improvement of the fusion technique proposed by Li et al.Results and conclusionBy comparing the segmentation results with manual method and built-in method of Corvis ST, the accuracy and robustness of our proposed segmentation method is demonstrated. The correctness of the estimated corneal anterior curvatures is also evaluated by comparing it with that of Pentacam which is considered to be able to provide the first-class measurement currently. The dynamic topography may be used to distinguish the dynamic behavior of normal corneas from that of keratoconus.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-015-0036-2) contains supplementary material, which is available to authorized users.

Anatomical location of gliomas has been considered as a factor implicating the contributions of a specific precursor cells during the tumor growth. Isocitrate dehydrogenase 1 (IDH1) is a pathognomonic biomarker with a significant impact on the development of gliomas and remarkable prognostic effect. The correlation between anatomical location of tumor and IDH1 states for low-grade gliomas was analyzed quantitatively in this study. Ninety-two patients diagnosed of low-grade glioma pathologically were recruited in this study, including 65 patients with IDH1-mutated glioma and 27 patients with wide-type IDH1. A convolutional neural network was designed to segment the tumor from three-dimensional magnetic resonance imaging images. Voxel-based lesion symptom mapping was then employed to study the tumor location distribution differences between gliomas with mutated and wild-type IDH1. In order to characterize the location differences quantitatively, the Automated Anatomical Labeling Atlas was used to partition the standard brain atlas into 116 anatomical volumes of interests (AVOIs). The percentages of tumors with different IDH1 states in 116 AVOIs were calculated and compared. Support vector machine and AdaBoost algorithms were used to estimate the IDH1 status based on the 116 location features of each patient. Experimental results proved that the quantitative tumor location measurement could be a very important group of imaging features in biomarker estimation based on radiomics analysis of glioma.

In ultrasound (US), optical coherence tomography, synthetic aperture radar, and other coherent imaging systems, images are corrupted by multiplicative speckle noise that obscures image interpretation. An anisotropic diffusion (AD) method based on the Gabor transform, named Gabor-based anisotropic diffusion (GAD), is presented to suppress speckle in medical ultrasonography. First, an edge detector using the Gabor transform is proposed to capture directionality of tissue edges and discriminate edges from noise. Then the edge detector is embedded into the partial differential equation of AD to guide the diffusion process and iteratively denoise images. To enhance GAD's adaptability, parameters controlling diffusion are determined from a fully formed speckle region that is automatically detected. We evaluate the GAD on synthetic US images simulated with three models and clinical images acquired in vivo. Compared with seven existing speckle reduction methods, the GAD is superior to other methods in terms of noise reduction and detail preservation.

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