Glaucoma is a widespread ocular disorder leading to irreversible loss of vision. Therefore, there is a pressing need for cost-effective screening, such that preventive measures can be taken. This can be achieved with an accurate segmentation of the optic disc and cup from retinal images to obtain the cup-to-disc ratio. We describe a comprehensive solution based on applying convolutional neural networks to feature exaggerated inputs emphasizing disc pallor without blood vessel obstruction, as well as the degree of vessel kinking. The produced raw probability maps then undergo a robust refinement procedure that takes into account prior knowledge about retinal structures. Analysis of these probability maps further allows us to obtain a confidence estimate on the correctness of the segmentation, which can be used to direct the most challenging cases for manual inspection. Tests on two large real-world databases, including the publicly-available MESSIDOR collection, demonstrate the effectiveness of our proposed system.

Automatic identification of the Frankfurt plane (FP) and mid-sagittal plane (MSP) of a human skull is a very important task. These planes are used to define important landmarks on the skull for surgery planning and guidance, forensic reconstruction, and anthropological and archaeological studies. At present, there is no known method for automatic identification of FP. Existing methods identify FP by fitting a plane over manually marked landmark points. On the other hand, existing automatic methods identify MSP by looking for a symmetric plane that divides the skull into the left and right sides. Unfortunately, this approach is inaccurate because extensive anthropological studies show that there is significant lateral asymmetry in the skulls. This paper proposes an automatic, accurate, and robust method for identifying FP and MSP of skulls. It registers a template skull model with known landmarks to a target skull to locate the landmarks, FP, and MSP on the target skull. Then, it iteratively refines the landmark locations, FP and MSP according to their medical definitions. Test results show that the proposed algorithm is more robust and accurate than symmetry-based methods. Moreover, it can also be applied to partial skull models of real patients.

This paper introduces a method to correct over-exposure in an existing photograph by recovering the color and lightness separately. First, the dynamic range of well exposed region is slightly compressed to make room for the recovered lightness of the over-exposed region. Then the lightness is recovered based on an over-exposure likelihood. The color of each pixel is corrected via neighborhood propagation and also based on the confidence of the original color. Previous methods make use of ratios between different color channels to recover the over-exposed ones, and thus can not handle regions where all three channels are over-exposed. In contrast, our method does not have this limitation. Our method is fully automatic and requires only one single input photo. We also provide users with the flexibility to control the amount of over-exposure correction. Experiment results demonstrate the effectiveness of the proposed method in correcting over-exposure.

Medical volume images are large in size. They cannot be efficiently transmitted and visualized as candidates for medical image retrieval and relevance feedback. On the other hand, 2D images that are small in size and rich in 3D details can be efficiently transmitted and visualized as candidates. This paper presents an algorithm that summarizes the 3D details in a volume image into a single 2D image. It applies soft segmentation to highlight the anatomy of interest in the volume, and automatically selects a salient view that contains the most amount of semantic information as the summarization of the volume image. Experimental results show that the proposed method can well summarize medical volume images of different anatomical structures. Compared to representation of volume images using 2D slices and conventional volume rendering, our summarized images are rich in 3D details, and they can be transmitted and visualized very efficiently.