3D Axon structure extraction and analysis in confocal fluorescence microscopy images

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.     

Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.     

Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images

Images of cellular structures in growing plant roots acquired using confocal laser scanning microscopy have some unusual properties that make motion estimation challenging. These include multiple motions, non-Gaussian noise and large regions with little spatial structure. In this paper, a method for motion estimation is described that uses a robust multi-frame likelihood model and a technique for estimating uncertainty. An efficient region-based matching approach was used followed by a forward projection method. Over small timescales the dynamics are simple (approximately locally constant) and the change in appearance small. Therefore, a constant local velocity model is used and the MAP estimate of the joint probability over a set of frames is recovered. Occurrences of multiple modes in the posterior are detected, and in the case of a single dominant mode, motion is inferred using Laplace’e method. The method was applied to several Arabidopsis thaliana root growth sequences with varying levels of success. In addition, comparative results are given for three alternative motion estimation approaches, the Kanade–Lucas–Tomasi tracker, Black and Anandan’s robust smoothing method, and Markov random field based methods.     

粪便镜检图像中红白细胞的分割与识别方法

针对粪便镜检图像中具有弱边界的红、白细胞的识别问题,研究了基于Chan-Vese模型的兼顾邻域区域边缘和纹理综合信息的分割方法。用八向Sobel弥补透明细胞的模糊边缘,通过细胞域内纹理和边缘信息互补而采用兼顾全局和局部能量分布的Chan-Vese模型的分割方法,并采用具备更好的数据泛化作用的随机决策森林进行分类。实验证明,提出的兼顾边界与域内纹理的改进型Chan-Vese分割方法使粪便镜检图像中红、白细胞的分割精度达到了95.3%。该方法对粪便镜检图像中的有形物体具备更高的分辨能力和光学环境适应性。     

A convolutional neural network model for semantic segmentation of mitotic events in microscopy images

Neural Computing and Applications - Mitosis, which has important effects such as healing and growing for human body, has attracted considerable attention in recent years. Especially, cell division...     

Digital image processing of confocal images

In a confocal imaging system, the image is built up point by point and is therefore well suited for image digitization and processing. A confocal system also allows range information to be obtained which facilitates an understanding of the 3D structure of the object. The results of several digital image processing techniques applied to images from a confocal scanning optical microscope are presented.     

3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images

Detailed and accurate characteristics of preimplantation embryos are fundamental for a deep understanding of their development. Recent studies indicate that various geometric features of cells, such as size, shape, volume, and position play a significant role in embryo growth. However, a quantitative assessment of these characteristics first needs a segmentation of the individual cells. The manual separation and labeling of cells is extremely inefficient, and an automated approach is highly desirable. This paper presents an automatic method for early stage embryo segmentation into its constituent cells and membranes using three-dimensional (3D) data. The input data consist of two Z-stacks of fluorescence microscope images containing nuclei and membranes. The method uses a 3D level set segmentation algorithm. Its evaluation is based on a dataset composed of 20 mouse embryos, each with 4–32 blastomeres. Segmentation accuracy was evaluated by calculating F-scores with ground truth obtained by manually labeling desired regions. We also compared output of our method with the one acquired with a watershed algorithm. The proposed approach was able to achieve more than \(90\%\) accuracy for embryos with 4 and 8 cells, while for embryos with higher number of cells it was lower, reaching \(75\%\) for 32-cell embryo.     

Multi-agent segmentation of IVUS images

A novel multi-agent image interpretation system has been developed which is markedly different from previous approaches in especially its elaborate high-level knowledge-based control over low-level image segmentation algorithms. Agents dynamically adapt segmentation algorithms based on knowledge about global constraints, contextual knowledge, local image information and personal beliefs. Generally agent control allows the underlying segmentation algorithms to be simpler and be applied to a wider range of problems with a higher reliability.The agent knowledge model is general and modular to support easy construction and addition of agents to any image processing task. Each agent in the system is further responsible for one type of high-level object and cooperates with other agents to come to a consistent overall image interpretation. Cooperation involves communicating hypotheses and resolving conflicts between the interpretations of individual agents.The system has been applied to IntraVascular UltraSound (IVUS) images which are segmented by five agents, specialized in lumen, vessel, calcified-plaque, shadow and sidebranch detection. IVUS image sequences from 7 patients were processed and vessel and lumen contours were detected fully automatically. These were compared with expert-corrected semiautomatically detected contours. Results show good correlations between agents and expert with r=0.84 for the lumen and r=0.92 for the vessel cross-sectional areas, respectively.     

Model-based segmentation of grey-tone images

A model-based approach to grey-tone image segmentation is presented. A conceptual and computational frame is described, in which a variety of image models can be accommodated. Each model is defined by a feature pair and implies a uniformity criterion for ideal regions. Some particularly relevant models are described in detail and illustrated by means of experimental results obtained with real-world images.     

Semantic segmentation of high-resolution images

Image semantic segmentation is a research topic that has emerged recently. Although existing approaches have achieved satisfactory accuracy, they are limited to handling low-resolution images owing to their large memory consumption. In this paper, we present a semantic segmentation method for high-resolution images. First, we downsample the input image to a lower resolution and then obtain a low-resolution semantic segmentation image using state-of-the-art methods. Next, we use joint bilateral upsampling to upsample the low-resolution solution and obtain a high-resolution semantic segmentation image. To modify joint bilateral upsampling to handle discrete semantic segmentation data, we propose using voting instead of interpolation in filtering computation. Compared to state-of-the-art methods, our method significantly reduces memory cost without reducing result quality.     

Histogram-based automatic segmentation of images

Neural Computing and Applications - The segmentation process is defined by separating the objects as clustering in the images. The most used method in the segmentation is k-means clustering...     

Edge-region-based segmentation of range images

In this correspondence, we present a new computationally efficient three-dimensional (3-D) object segmentation technique. The technique is based on the detection of edges in the image. The edges can be classified as belonging to one of the three categories: fold edges, semistep edges (defined here), and secondary edges. The 3-D image is sliced to create equidepth contours (EDCs). Three types of critical points are extracted from the EDCs. A subset of the edge pixels is extracted first using these critical points. The edges are grown from these pixels through the application of some masks proposed in this correspondence. The constraints of the masks can be adjusted depending on the noise present in the image. The total computational effort is small since the masks are applied only over a small neighborhood of critical points (edge regions). Furthermore, the algorithm can be implemented in parallel, as edge growing from different regions can be carried out independently of each other     

Turbo segmentation of textured images

We consider the problem of semi-supervised segmentation of textured images. Existing model-based approaches model the intensity field of textured images as a Gauss-Markov random field to take into account the local spatial dependencies between the pixels. Classical Bayesian segmentation consists of also modeling the label field as a Markov random field to ensure that neighboring pixels correspond to the same texture class with high probability. Well-known relaxation techniques are available which find the optimal label field with respect to the maximum a posteriori or the maximum posterior mode criterion. But, these techniques are usually computationally intensive because they require a large number of iterations to converge. In this paper, we propose a new Bayesian framework by modeling two-dimensional textured images as the concatenation of two one-dimensional hidden Markov autoregressive models for the lines and the columns, respectively. A segmentation algorithm, which is similar to turbo decoding in the context of error-correcting codes, is obtained based on a factor graph approach. The proposed method estimates the unknown parameters using the Expectation-Maximization algorithm.     

多图像融合的连续腹腔影像图像分割

为实现连续腹腔影像图像分割的实时性和准确性,提出多图像融合的水平集图像分割模型。该模型通过Chan-Vese模型在预分割图像基础上获取形状信息,同时利用Li模型进一步在原始图像上获取边缘信息,以提取腹腔影像图中感兴趣区域;对相邻且变化缓慢的连续腹腔影像图,可将前一幅的分割结果作为下一幅的预分割图像,从而提高连续影像图像的分割效率。初步实验结果表明,该模型能实现目标区域相对连通的腹腔影像图像的有效分割,并且在处理连续腹腔影像图时处理效率较传统的方法有较大提高。     

Resolution enhancement in three-dimensional confocal microscopy

We discuss various methods of tuning and improving both the optical sectioning strength and the lateral resolution of the confocal scanning optical microscope. Techniques based on using different wavelengths, pupil plane filters, different sized detectors and laterally displaced detectors are considered and experimental results presented. Both bright field reflection and fluorescence imaging are discussed.     

An active-contour based algorithm for the automated segmentation of dense yeast populations on transmission microscopy images

An image-processing pipeline for the automated segmentation of yeast cells in microscopy images is proposed. The method is suitable for the non-invasive detection of individual cells in transmission data which can be acquired simultaneously with fluorescence data. It moreover takes the varying quality and highly heterogeneous characteristics of cells in transmission images into account, is capable to process images with dense yeast populations and can be used to extract quantitative cell-based data from transmission/fluorescence image pairs. Applicability and performance of the method is evaluated on a data set of 523 different yeast deletion mutant strains.     

3-D aggregated object detection and labeling from multivariate confocal microscopy images: a model validation approach

One essential assumption used in object detection and labeling by imaging is that the photometric properties of the object are homogeneous. This homogeneousness requirement is often violated in microscopy imaging. Classical methods are usually of high computational cost and fail to give a stable solution. This paper presents a low computational complexity and robust method for three-dimensional (3-D) biological object detection and labeling. The developed approach is based on a statistical, nonparametric framework. Image is first divided into regular nonoverlapped regions and each region is evaluated according to a general photometric variability model. The regions not consistent with this model are considered as aberration in the data and excluded from the analysis procedure. Simultaneously, the interior parts of the object are detected, they correspond to regions where the supposed model is valid. In the second stage, the valid regions from a same object are merged together depending on a set of hypotheses. These hypotheses are generated by taking into account photometric and geometric properties of objects of interest and the merging is achieved according to an iterative algorithm. The approach has been applied in investigations of spatial distribution of nuclei within colonic glands of rats observed with the help of confocal fluorescence microscopy.     

Multiple resolution segmentation of textured images

A multiple resolution algorithm is presented for segmenting images into regions with differing statistical behavior. In addition, an algorithm is developed for determining the number of statistically distinct regions in an image and estimating the parameters of those regions. Both algorithms use a causal Gaussian autoregressive model to describe the mean, variance, and spatial correlation of the image textures. Together, the algorithms can be used to perform unsupervised texture segmentation. The multiple resolution segmentation algorithm first segments images at coarse resolution and then progresses to finer resolutions until individual pixels are classified. This method results in accurate segmentations and requires significantly less computation than some previously known methods. The field containing the classification of each pixel in the image is modeled as a Markov random field. Segmentation at each resolution is then performed by maximizing the a posteriori probability of this field subject to the resolution constraint. At each resolution, the a posteriori probability is maximized by a deterministic greedy algorithm which iteratively chooses the classification of individual pixels or pixel blocks. The unsupervised parameter estimation algorithm determines both the number of textures and their parameters by minimizing a global criterion based on the AIC information criterion. Clusters corresponding to the individual textures are formed by alternately estimating the cluster parameters and repartitioning the data into those clusters. Concurrently, the number of distinct textures is estimated by combining clusters until a minimum of the criterion is reached     

Automated segmentation of brain MR images

A simple, robust and efficient image segmentation algorithm for classifying brain tissues from dual echo Magnetic Resonance (MR) images is presented. The algorithm consists of a sequence of adaptive histogram analysis, morphological operations and knowledge based rules to accurately classify various regions such as the brain matter and the cerebrospinal fluid, and detect if there are any abnormal regions. It can be completely automated and has been tested on over hundred images from several patient studies. Experimental results are provided.     

Unsupervised multiscale segmentation of color images

This paper proposes a new multiresolution technique for color image representation and segmentation, particularly suited for noisy images. A decimated wavelet transform is initially applied to each color channel of the image, and a multiresolution representation is built up to a selected scale 2^J. Color gradient magnitudes are computed at the coarsest scale 2^J, and an adaptive threshold is used to remove spurious responses. An initial segmentation is then computed by applying the watershed transform to thresholded magnitudes, and this initial segmentation is projected to finer resolutions using inverse wavelet transforms and contour refinements, until the full resolution 2⁰ is achieved. Finally, a region merging technique is applied to combine adjacent regions with similar colors. Experimental results show that the proposed technique produces results comparable to other state-of-the-art algorithms for natural images, and performs better for noisy images.