Gibbs distribution-based Bayesian segmentation of electron microscopy nanostructure images

The use of Gibbs distribution-based Bayesian segmentation of electron microscopy images for visualizing nanostructures is investigated. Bayesian segmentation involves dividing an image into nonoverlapping areas that correspond as closely as possible to the observed image. A quantitative characteristic of this correspondence is the a posteriori probability of one variant of division or another. The most likely version is always the division with the greatest a posteriori probability. The Metropolis algorithm for stochastic relaxation is used to obtain Bayesian estimates of the a posteriori probability of a division. Our study of Bayesian segmentation requires visualization of nanostructures on an electron microscopy image of a film made of NiW nanocrystalline alloy.     

A two-stage method to correct aberrations induced by slide slant in bright-field microscopy

To achieve optimal image quality in bright field microscopy, the slide surface should be perpendicular to the optical axis of the microscope. However, in the recently proposed “slanted scan” slide acquisition technique, scan speed is increased by purposely slanting the slide by a small angle (of 3–5°) so that multiple focal depths can be imaged simultaneously. In this case, the slanted slide introduces a bend in the point spread function (PSF), resulting in a coma and other aberrations that degrade image quality. In this paper, we propose a two-stage deconvolution method specifically designed to correct the aberrations induced by a slanted scan, but with general applicability to high-resolution bright-field microscopy. Specifically, we initially apply phase deconvolution to correct the dominating coma aberration, before applying a conventional semi-blind deconvolution method to further improve image resolution and contrast. We also propose a novel method to estimate the degree of coma aberration and the PSF of the optics utilising actual cytology specimens. The efficacy of the proposed algorithm is demonstrated quantitatively on simulated data, against a ground-truth (object) image, and qualitatively on cervical cytology specimens. Results demonstrate both improved convergence speed of the two-stage approach, especially when correcting the bend in the PSF, and a resultant image quality that is comparable to a conventionally (flat) scanned specimen.     

Modeling monofractal microscopy images

Methods for modeling monofractal microscopy images on the basis of space-frequency filtration are proposed. The spectral density and structural function of the monofractal images are assumed to be constant. Space-frequency filtration of the initial image is used for modeling; it transforms the amplitude distribution so that the integral frequency characteristic of the Fourier spectrum of individual segments is modified into a function that diminishes in accordance with a power law. The degree to which it diminishes for each segment is selected to ensure that estimates of the structural functions of the first and higher orders in a double logarithmic scale are approximated well by linear dependencies. The possibilities of our methods are demonstrated by modeling monofractal images of nanostructures for samples of amorphous alloys obtained via electrolytic deposition.     

复杂背景彩色图像中的文字分割

提出了一种纹理和连通域特征相结合的彩色文字分割方法。基于文字的边缘纹理特征,粗略分割出可能的文字区域。计算各区域内的颜色统计直方图,进行二类颜色聚类。分析文字连通域的几何特征,滤除非文字连通域。计算文字连通域像素点的垂直投影,估计文字的宽度和文字间隔,判断是否为文字排列,对粗分割的文字区域进行校验,确定文字区域的边框。通过自然场景下拍摄的100幅图像的文字分割实验,证明了该文字定位和分割方法的有效性。     

Automated segmentation of optical coherence tomography images

We propose a fast and accurate automated algorithm to segment retinal pigment epithelium and internal limiting membrane layers from spectral domain optical coherence tomography(SDOCT) B-scan images. A hybrid algorithm, which combines intensity thresholding and graph-based algorithms, was used to process and analyze SDOCT radial scans(120 B scans) images obtained from twenty patients. The relative difference in position of the layers segmented by the proposed hybrid algorithm and by the clinical expert was 1.49% ± 0.01%. The processing time of the hybrid algorithm was 9.3 s for six B scans. Dice's coefficient of the hybrid algorithm was 96.7% ± 1.6%. The proposed hybrid algorithm for the segmentation of SDOCT images had good agreement with manual segmentation and reduced processing time.     

An anisotropic images segmentation and bias correction method

Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field correction is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents an anisotropic approach to bias correction and segmentation for images with intensity inhomogeneities and noise. Intensity-based methods are usually applied to estimate the bias field; however, most of them only concern the intensity information. When the images have noise or slender topological objects, these methods cannot obtain accurate results or bias fields. We use structure information to construct an anisotropic Gibbs field and combine the anisotropic Gibbs field with the Bayesian framework to segment images while estimating the bias fields. Our method is able to capture bias of quite general profiles. Moreover, it is robust to noise and slender topological objects. The proposed method has been used for images of various modalities with promising results.     

Time-resolved thermal microscopy with fluorescent films

A technique is experimentally demonstrated, allowing one, with a time resolution of a few ns, to remotely measure the temperature distribution on surfaces that are accessible to light. The surfaces are coated with a polymer containing fluorescing dye. When excited at the desired moment with a flashlight this is induced to fluoresce. As the fluorescence yield is temperature dependent, the temperature distribution during the fluorescence decay time can be uniquely determined. The fluorescence is recorded first in thermal equilibrium and then at the elevated temperature for which knowledge of the temperature distribution is desired. From the ratio of the spatially resolved fluorescence yields the temperature distribution can be evaluated. With spatial resolutions down to 2 m, temperatures in the range 20°–120°C are measured with accuracies of ±1°C, where the measurement intervals are as short as 18 ns.Dedicated to Prof. Dr. Herbert Welling on the occasion of his 60th birthday     

Neural network-based segmentation of dynamic MR mammographic images

The usefulness of neural networks for the classification of signal-time curves from dynamic MR mammography was recently demonstrated by our group. The multi-layer perceptron under study consists of 28 input, 4 hidden, and 3 output nodes, and was trained to classify signal-time curves into three tissue classes: "carcinoma," "benign lesion," and "parenchyma." Extending this approach, it was the aim of the present study to evaluate the performance of the developed network in the segmentation of dynamic MR mammographic images in comparison to a pixel-by-pixel two-compartment pharmacokinetic analysis. The population investigated in this pilot study comprised 15 women with suspicious lesions in the breast, which were confirmed histologically after the MR examination. The neural network classified the same areas as malignant as those which were marked as being highly suspicious by the pharmacokinetic mapping approach but with the advantage that no a priori knowledge on tissue microcirculation was needed, that computation proved to be much faster, and that it yielded a unique classification into just three tissue classes.     

Three-dimensional snow images by MR microscopy

MR microscopy technique was introduced to visualize and quantify the three-dimensional structure of snowpack. Since the NMR signal from the ice was week, we looked at the air space instead filling with dodecane or aniline doped with iron acetylacetonate. Four types of snow were tested: ice spheres, large rounded poly crystals, small rounded mono-crystals and depth hoar crystals. A specific specimen-cooling system was developed to keep the temperature below 0 degrees C. In the experiments 0.5 to 2 h were necessary to accumulate the signals enough to obtain a 3D micro-image; the image matrix 128(3), voxel size (200 microm)3 or 256(3) (120 microm)3. Comparison with the 2D data using the conventional section plane method was also carried out and MR microscopy is proved to be a very useful method to visualize the microstructure of snowpack.     

Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening

The hippocampus has been known to be an important structure as a biomarker for Alzheimer’s disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index = 0.81 ± 0.03) than the conventional atlas-based segmentation method (0.72 ± 0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision = 0.76 ± 0.04, recall = 0.86 ± 0.05) produced lower ratios than the conventional methods (0.73 ± 0.05, 0.72 ± 0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus.     

Iris segmentation for visible wavelength and near infrared eye images

State-of-the-art iris segmentation algorithms exhibit poor performance for non-ideal data, which is mainly because of the noise such as low contrast, non-uniform illumination, reflections, and among others. To address this issue, a robust iris segmentation scheme is proposed that includes the following: First, a set of the Seed-pixels in a preprocessed eye image is marked adaptively. Next, a two-fold scheme based on a Circu-differential accumulator (CDA) and gray statistics is adopted to localize coarse iris region robustly. Notably, the proposed CDA has close resemblance with the Hough transform; however, it consumes relatively less memory and is free from thresholding as well. Similarly, pupillary boundary is localized, which is verified through an intensity test as well. Next, a refine estimate for the limbic boundary is extracted. After that, iris boundaries are regularized using the Fourier series. Finally, the eyelids are localized using a Para-differential accumulator (PDA), and eyelashes and reflections are also localized adaptively in the polar form of iris. Experimental results on the near infrared (NIR) and visible wavelength (VW) iris databases show that the proposed technique outperforms contemporary approaches.     

Segmentation and counting of FISH signals in confocal microscopy images

In this paper we have presented a semi-automatic method for segmenting and counting the Fluorescence In Situ Hybridization (FISH) signals per cell nucleus in a 3D tissue image. The number of FISH signals indicate the gain (trisomy) or loss (monosomy) of certain base-sequences in deoxyribonucleic acid (DNA). The quantitative evaluation of the loss or gain in DNA is necessary in qualitative diagnostic molecular pathology. Multispectral volumetric images are obtained using the Confocal Microscope. Each consists of a red channel depicting the 3D morphology of the tissue and green channel containing the FISH signals. The red channel tissue image is segmented first to determine the membership of the FISH signal to a particular nuclei. Various segmentation methods starting from simple local thresholding to volume growing and active volumes are used for segmentation. A brief comparative study of the visual signal count by pathologist and our automatic count is also presented.     

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.     

Semiautomated segmentation of the human spine based on echoplanar images

The number of functional magnetic resonance imaging (fMRI) studies performed on the human spinal cord (SC) has considerably increased in recent years. The lack of a validated processing pipeline is, however, a significant obstacle to the spread of SC fMRI. One component likely to be involved in any such pipeline is the process of SC masking, analogous to brain extraction in cerebral fMRI. In general, SC masking has been performed manually, with the incumbent costs of being very time consuming and operator dependent. To overcome these drawbacks, we have developed a tailored semiautomatic method for segmenting echoplanar images (EPI) of human spine that is able to identify the spinal canal and the SC. The method exploits both temporal and spatial features of the EPI series and was tested and optimized on EPI images of cervical spine acquired at 3 T. The dependence of algorithm performance on the degree of EPI image distortion was assessed by computing the displacement warping field that best matched the EPI to the corresponding high-resolution T(2) images. Segmentation accuracy was above 80%, a significant improvement over values obtained with similar approaches, but not exploiting temporal information. Geometric distortion was found to explain about 50% of the variance of algorithm classification efficiency.     

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.     

Stochastic segmentation of severely degraded images using gibbs random fields

This paper deals with segmentation of noisy images using Gibbs random field (GRF) with an emphasis on modeling of the region process. For noisy image segmentation using the multi-level logistic (MLL) model with the second-order neighborhood system, which is commonly used in image processing, the segmentation performance is degraded significantly in case of low signal to noise ratio. By comparison with the Ising model that explains the magnetic properties of ferromagnetic material, it is evident that the characteristics of the region process modeled using the MLL model with the second-order neighborhood system are different in nature from the expected characteristics of a region. To solve this problem we added the term of the magnetic energy associated with the magnetic field of a spin system (or image) to the energy function of GRF. Using the modified model for the region process, the result of image segmentation was improved and did not depend on the cooling schedule in simulated annealing.     

Texture based segmentation of epithelial layer from oral histological images

The objective of this paper is to provide a texture based segmentation algorithm for better delineation of the epithelial layer from histological images in discriminating normal and oral sub-mucous fibrosis (OSF). As per literature and oral clinicians, it is established that the OSF initially originates and propagates in the epithelial layer. So, more accurate segmentation of this layer is extremely important for a clinician to make a diagnostic decision. In doing this, Gabor based texture gradient is computed in gray scale images, followed by preprocessing of the microscopic images of oral histological sections. On the other hand, the color gradients of these images are obtained in the transformed Lab color space. Finally, the watershed segmentation is extended to segment the layer based on the combination of texture and color gradients. The segmented images are compared with the ground truth images provided by the oral experts. The segmentation results depict the superiority of the texture based segmentation in comparison to the Otsu's based segmentation in terms of misclassification error. Results are shown and discussed.     

Digital holography-assisted 3-D bright-field image reconstruction and refocusing

Optical Review - We propose an image reconstruction technique of a color 3-D object to perform image refocusing by combining information from bright-field image and digital holography. The key...     

Interpretation of scanning tunneling microscopy and atomic force microscopy images of 1T-TiS2

The surface of 1T-TiS₂ was examined by scanning tunneling microscopy (STM) and atomic force microscopy (AFM). The STM and AFM images of this compound were interpreted on the basis of the partial electron density ρ(r,E_F) and total electron density ρ(r) of a slab which consists of six (001) 1T-TiS₂ layers. Electronic structure calculations were performed using the ab-initio Hartree–Fock program crystal. It was found that the bright spots in experimental STM images correspond to sulfur atoms at both positive and negative bias voltages. The AFM image showed a periodicity which can be explained by the atomic corrugation at the surface. Structural defects on the surface were also investigated, and their interpretation constitutes experimental proof that only sulfur atoms were detected by scanning probe microscopies.     

Quality evaluation of microscopy and scanned histological images for diagnostic purposes

In this work we present a study for assessing and comparing the fidelity of biopsy and cytology images captured with two different devices, that is optical microscopes and scanners, at 40× magnification in bright field. The devices use different ways to magnify images. Microscopes use a set of lenses while scanners capture light through arrays of micro-photoreceptors. The objective is to carry out a quantitative evaluation to discern which of the two devices provides better image quality in terms of contrast, colour and stain. Since there is no unanimous consensus on quality metrics, we will make use of both an objective metric based on perceptual features, together with a subjective psychophysical test as the International Telecommunications Union (ITU) recommends in ITU-R BT.500 for such type of tests. Both techniques indicate a slight preference for the scanner over the microscope in terms of better image quality, considering defocus as the main problem followed by colour distortions. However, the image quality of both devices is suitable for clinical, educational and research purposes.