Fusion of GFP and phase contrast images with complex shearlet transform and Haar wavelet-based energy rule.

Image fusion techniques can integrate the information from different imaging modalities to get a composite image which is more suitable for human visual perception and further image processing tasks. Fusing green fluorescent protein (GFP) and phase contrast images is very important for subcellular localization, functional analysis of protein and genome expression. The fusion method of GFP and phase contrast images based on complex shearlet transform (CST) is proposed in this paper. Firstly the GFP image is converted to IHS model and its intensity component is obtained. Secondly the CST is performed on the intensity component and the phase contrast image to acquire the low-frequency subbands and the high-frequency subbands. Then the high-frequency subbands are merged by the absolute-maximum rule while the low-frequency subbands are merged by the proposed Haar wavelet-based energy (HWE) rule. Finally the fused image is obtained by performing the inverse CST on the merged subbands and conducting IHS-to-RGB conversion. The proposed fusion method is tested on a number of GFP and phase contrast images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation.

A computational approach to detect and segment cytoplasm in muscle fiber images.

We developed a computational approach to detect and segment cytoplasm in microscopic images of skeletal muscle fibers. The computational approach provides computer-aided analysis of cytoplasm objects in muscle fiber images to facilitate biomedical research. Cytoplasm in muscle fibers plays an important role in maintaining the functioning and health of muscular tissues. Therefore, cytoplasm is often used as a marker in broad applications of musculoskeletal research, including our search on treatment of muscular disorders such as Duchenne muscular dystrophy, a disease that has no available treatment. However, it is often challenging to analyze cytoplasm and quantify it given the large number of images typically generated in experiments and the large number of muscle fibers contained in each image. Manual analysis is not only time consuming but also prone to human errors. In this work we developed a computational approach to detect and segment the longitudinal sections of cytoplasm based on a modified graph cuts technique and iterative splitting method to extract cytoplasm objects from the background. First, cytoplasm objects are extracted from the background using the modified graph cuts technique which is designed to optimize an energy function. Second, an iterative splitting method is designed to separate the touching or adjacent cytoplasm objects from the results of graph cuts. We tested the computational approach on real data from in vitro experiments and found that it can achieve satisfactory performance in terms of precision and recall rates.

An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images.

Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio-marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre-processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two-step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images.

Implementation of long-wavelength cut-off filters based on critical angle.

Our first attempts at the fabrication of long-wavelength infrared cut-off filters with extended transmission and rejection regions that are based on the use of the critical angle, the dispersion of refractive indices, and on thin-film interference were not very successful. The design of the filter consisted of layers placed at the interface between two high-index prisms. Using the available deposition equipment, the layers produced were porous and very rough. The pores adsorbed water vapor, which resulted in absorption. The roughness made the process of optical contacting very difficult. In this paper we describe the adjustments in the design and deposition processes that allowed us to obtain filters with a better and more stable performance.

Toward perfect antireflection coatings. 3. Experimental results obtained with the use of Reststrahlen materials.

The equipment and methods used to produce wide-angle antireflection coatings based on Reststrahlen materials are described. The optical constants of the coating materials used in the construction of the multilayers were determined by spectrophotometric ellipsometry and are compared with the literature values. The measured performance of an experimentally produced antireflection coating is compared with the expected calculated performance. The reflectance is low over a wide range of angles, but only in the narrow-wavelength region at which the refractive index of the Reststrahlen material is close to unity.