Label-Free Tracking of Proteins through Plasmon-Enhanced Interference.

Single unmodified biomolecules in solution can be observed and characterized by interferometric imaging approaches; however, Rayleigh scattering limits this to larger proteins (typically >30 kDa). We observe real-time image tracking of unmodified proteins down to 14 kDa using interference imaging enhanced by surface plasmons launched at an aperture in a metal film. The larger proteins show slower diffusion, quantified by tracking. When the diffusing protein is finally trapped by the nanoaperture, we perform complementary power spectral density and noise amplitude analysis, which gives information about the protein. This approach allows for rapid protein characterization with minimal sample preparation and opens the door to characterizing protein interactions in real time.

Automatic segmentation and tracking of biological prosthetic heart valves.

Purpose: Prosthetic heart valve designs must be rigorously tested using cardiovascular equipment. The valve orifice area over time constitutes a key quality metric which is typically assessed manually, thus a tedious and error-prone task. From a computer vision viewpoint, a major unsolved issue lies in the orifice being partly occluded by the leaflets' inner side or inaccurately depicted due to its transparency. Here, we address this issue, which allows us to focus on the accurate and automatic computation of valve orifice areas. Approach: We propose a segmentation approach based on the detection of the leaflets' free edges. Using video frames recorded with a high-speed digital camera during in vitro simulations, an initial estimation of the orifice area is first obtained via active contouring and thresholding and then refined to capture the leaflet free edges via a curve transformation mechanism. Results: Experiments on video data from pulsatile flow testing demonstrate the effectiveness of our approach: a root-mean-square error (RMSE) on the temporal extracted orifice areas between 0.8% and 1.2%, an average Jaccard similarity coefficient between 0.933 and 0.956, and an average Hausdorff distance between 7.2 and 11.9 pixels. Conclusions: Our approach significantly outperformed a state-of-the-art algorithm in terms of evaluation metrics related to valve design (RMSE) and computer vision (accuracy of the orifice shape). It can also cope with lower quality videos and is better at processing frames showing an almost closed valve, a crucial quality for assessing valve design malfunctions related to their improper closing.

A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images.

Underwater images are often acquired in sub-optimal lighting conditions, in particular at profound depths where the absence of natural light demands the use of artificial lighting. Low-lighting images impose a challenge for both manual and automated analysis, since regions of interest can have low visibility. A new framework capable of significantly enhancing these images is proposed in this article. The framework is based on a novel dehazing mechanism that considers local contrast information in the input images, and offers a solution to three common disadvantages of current single image dehazing methods: oversaturation of radiance, lack of scale-invariance and creation of halos. A novel low-lighting underwater image dataset, OceanDark, is introduced to assist in the development and evaluation of the proposed framework. Experimental results and a comparison with other underwater-specific image enhancement methods show that the proposed framework can be used for significantly improving the visibility in low-lighting underwater images of different scales, without creating undesired dehazing artifacts.

Local image enhancement for fiducial marker detection in electronic portal images of prostate radiotherapy.

This paper proposes a new method for the automatic contrast enhancement of fiducial markers in low-radiation Electronic Portal Images. It is shown that the proposed approach significantly enhances the contrast of the fiducial markers and produces results where these markers are clearly visible. The main theoretical contribution consists in designing an algorithm that enhances the contrast of small structures in noisy images; the parameters of this algorithm are not empirically selected, but determined via a maximum search over a contrast metric. From a practical standpoint, the proposed method has direct applications in the current clinical workflow involving manual marker detection. It is also able to significantly improve the performances of automatic marker detection reported in literature.

A web-based remote collaborative system for visualization and assessment of semi-automatic diagnosis of liver cancer from CT images.

We propose a web-based collaborative CAD system allowing for the remote communication and data exchange between radiologists and researchers in computer vision-based software engineering. The proposed web-based interface is implemented in the Java Advanced Imaging Application Programming Interface. The different modules of the interface allow for 3D and 2D data visualization, as well as for the parametric adjustment of 3D reconstruction process. The proposed web-based CAD system was tested in a pilot study involving a limited number of liver cancer cases. The successful system validation in the feasibility stage will lead to an extended clinical study on CT and MR image databases.