A two-stage adaptive thresholding segmentation for noisy low-contrast images

Image recognition is the process of recognizing and classifying objects with machine learning algorithms. Image binarization is the first and most challenging step in image recognition, in which foreground objects are separated from their background. When foreground objects have complex morphological structure and background noise is strong, foreground objects are often being fractured into subcomponents. To address the over-segmentation issue of organisms with complex structures, we propose a 2-stage adaptive binarization approach based on Sauvola's binarization algorithm. We tested the effectiveness of the new approach on a set of underwater images with jellyfish collected in nearshore waters using a shadowgraph underwater plankton imaging system, PlanktonScope, because jellyfish have relatively complex structure and are often over-segemented. The results showed that the 2-stage approach improved the integrity of extracted jellyfish compared to traditional binarization methods, including Sauvola's algorithm. The analysis of local entropy values showed that the first stage effectively suppresses redundant information in the image and reduces the number of Region of Interests (ROIs), and the second stage preserves relatively weak and low-intensity signals to ensure the integrity of the extracted targets. The 2-stage approach improves hardware resource utilization and computational efficiency. It is robust for images acquired in sub-optimal conditions and enhances the accuracy of analytical results in the study of marine organisms using imaging systems.     

Nuclear segmentation of bronchial epithelial cells by minimax and thresholding techniques. A comparison

Two nuclear segmentation methods, Baky's minimax algorithm and thresholding, were compared on a sample of 879 atypical bronchial epithelial cells in sputum. Nuclear-cytoplasmic (N/C) ratios for all cells were determined by each segmentation method and compared to a visually determined value. Cells were categorized by atypia class (from metaplastic through malignant), by staining characteristics (orangeophilic and nonorangeophilic) and by method of digitization (either scanning microphotometry or video system). The method of digitization was confounded by subject differences. The results indicated that with most classes of atypia, N/C ratios determined by minimax were closer to the visually derived values than were those of thresholding, particularly with orangeophilic cells. Both methods become progressively less accurate, as compared to the visual procedure, as the degree of atypia increases.     

Topology-based fluorescence image analysis for automated cell identification and segmentation

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.     

A framework for automatic heart sound analysis without segmentation

Background  

A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs.     

A watershed approach for improving medical image segmentation

In this paper, a novel watershed approach based on seed region growing and image entropy is presented which could improve the medical image segmentation. The proposed algorithm enables the prior information of seed region growing and image entropy in its calculation. The algorithm starts by partitioning the image into several levels of intensity using watershed multi-degree immersion process. The levels of intensity are the input to a computationally efficient seed region segmentation process which produces the initial partitioning of the image regions. These regions are fed to entropy procedure to carry out a suitable merging which produces the final segmentation. The latter process uses a region-based similarity representation of the image regions to decide whether regions can be merged. The region is isolated from the level and the residual pixels are uploaded to the next level and so on, we recall this process as multi-level process and the watershed is called multi-level watershed. The proposed algorithm is applied to challenging applications: grey matter–white matter segmentation in magnetic resonance images (MRIs). The established methods and the proposed approach are experimented by these applications to a variety of simulating immersion, multi-degree, multi-level seed region growing and multi-level seed region growing with entropy. It is shown that the proposed method achieves more accurate results for medical image oversegmentation.     

基于K-means聚类算法的叶螨图像分割与识别

【目的】叶螨(spider mite)是为害多种农作物的主要害虫,叶螨识别传统方法依靠肉眼,比较费时费力,为研究快速自动识别方法,引入计算机图像分析算法。【方法】该方法基于K-means聚类算法对田间作物上的叶螨图像进行分割与识别。【结果】对比传统RGB彩色分割方法,K-means聚类算法能够有效地对叶片上叶螨图像进行分割和识别。K-means聚类算法平均识别时间为3.56 s,平均识别准确率93.95%。识别时间 T 随图像总像素 Pi 的增加而增加。【结论】K-means聚类组合算法能够应用于叶螨图像分割与识别。     

A statistically driven approach for image segmentation and signal extraction in cDNA microarrays.

The increasing use of cDNA microarrays necessitates the development of methods for extracting quality data. Here, we set forth hurdles to overcome in image analysis of microarrays. We emphasize the importance of objective data extraction methods resulting in reliable signal estimates. Based on statistical principles, we describe a method for automated grid alignment, spot detection, background estimation, flagging, and signal extraction. A software application that we call SignalViewer has been implemented for this method. We identify areas where we improved upon current methods used for array image analysis at each step in the process. Finally, we give examples to illustrate the performance of our algorithms on raw data.     

Calculus of variations and image segmentation.

A survey of free discontinuity problems related to image segmentation is given. The main properties and open problems about Mumford and Shah and Blake and Zisserman functionals are shown together with an extensive bibliography about recent mathematical developments.     

A shape-based method for automatic and rapid segmentation of roots in soil from X-ray computed tomography images: Rootine

Plant and Soil - X-ray computed tomography (CT) is widely recognized as a powerful tool for in-situ quantification of root system architecture (RSA) in soil. However, employing X-ray CT to identify...     

FogBank: a single cell segmentation across multiple cell lines and image modalities

Background

Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results

We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions

FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0431-x) contains supplementary material, which is available to authorized users.     

A network of coupled chaotic maps for adaptive multi-scale image segmentation

In this paper, a network of coupled chaotic maps for multi-scale image segmentation is proposed. Time evolutions of chaotic maps that correspond to a pixel cluster are synchronized with one another, while this synchronized evolution is desynchronized with respect to time evolution of chaotic maps corresponding to other pixel clusters in the same image. The number of pixel clusters is previously unknown and the adaptive pixel moving technique introduced in the model makes it robust enough to classify ambiguous pixels.     

A new method for cell immobilization.

A new method for producing particles and membranes containing immobilized bacteria is presented. These immobilized bacteria display good stability over time making them well suited for use in a packed-bed reactor. Such a reactor is tested as a function of the different parameters of the system. The results are qualitatively similar to those obtained with purified enzyme reactors, but some discrepancies with the plug-flow model are noted. It is necessary to use a more sophisticated model in order to fit the experimental data.     

An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.     

An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding

Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.     

A two-step procedure for automatic and accurate segmentation of volumetric CLSM biofilm images

This paper presents a robust two-step segmentation procedure for the study of biofilm structure. Without user intervention, the procedure segments volumetric biofilm images generated by a confocal laser scanning microscopy (CLSM). This automated procedure implements an anisotropic diffusion filter as a preprocessing step and a 3D extension of the Otsu method for thresholding. Applying the anisotropic diffusion filter to even low-contrast CLSM images significantly improves the segmentation obtained with the 3D Otsu method. A comparison of the results for several CLSM data sets demonstrated that the accuracy of this procedure, unlike that of the objective threshold selection algorithm (OTS), is not affected by biofilm coverage levels and thus fills an important gap in developing a robust and objective segmenting procedure. The effectiveness of the present segmentation procedure is shown for CLSM images containing different bacterial strains. The image saturation handling capability of this procedure relaxes the constraints on user-selected gain and intensity settings of a CLSM. Therefore, this two-step procedure provides an automatic and accurate segmentation of biofilms that is independent of biofilm coverage levels and, in turn, lays a solid foundation for achieving objective analysis of biofilm structural parameters.     

Iterative image transformations for an automatic screening of cervical smears.

The new generation of image analysis systems permits the use of iterative image transformations. It is now possible to construct algorithms where the elementary steps are not arithmetic operations but image transformations. This will be illustrated by two examples. In the first, the absorption image of Feulgen Stained nuclei is processed by contrast algorithms in order to detect suspect cells. In the second, free lying cells are separated from overlapping cells and other artefacts by the use of skeletonization procedures.     

A review of deep learning based methods for medical image multi-organ segmentation

Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are ‘pixel-wise classification’ and ‘end-to-end segmentation’. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.     

Automated image detection and segmentation in blood smears.

A simple technique which automatically detects and then segments nucleated cells in Wright's giemsa-stained blood smears is presented. Our method differs from others in 1) the simplicity of our algorithms; 2) inclusion of touching (as well as nontouching) cells; and 3) use of these algorithms to segment as well as to detect nucleated cells employing conventionally prepared smears. Our method involves: 1) acquisition of spectral images; 2) preprocessing the acquired images; 3) detection of single and touching cells in the scene; 4) segmentation of the cells into nuclear and cytoplasmic regions; and 5) postprocessing of the segmented regions. The first two steps of this algorithm are employed to obtain high-quality images, to remove random noise, and to correct aberration and shading effects. Spectral information of the image is used in step 3 to segment the nucleated cells from the rest of the scene. Using the initial cell masks, nucleated cells which are just touching are detected and separated. Simple features are then extracted and conditions applied such that single nucleated cells are finally selected. In step 4, the intensity variations of the cells are then used to segment the nucleus from the cytoplasm. The success rate in segmenting the nucleated cells is between 81 and 93%. The major errors in segmentation of the nucleus and the cytoplasm in the recognized nucleated cells are 3.5% and 2.2%, respectively.     

Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation

BACKGROUND: Confocal laser scanning microscopy (CLSM) presents the opportunity to perform three-dimensional (3D) DNA content measurements on intact cells in thick histological sections. So far, these measurements have been performed manually, which is quite time-consuming. METHODS: In this study, an intuitive contour-based segmentation algorithm for automatic 3D CLSM image cytometry of nuclei in thick histological sections is presented. To evaluate the segmentation algorithm, we measured the DNA content and volume of human liver and breast cancer nuclei in 3D CLSM images. RESULTS: A high percentage of nuclei could be segmented fully automatically (e.g., human liver, 92%). Comparison with (time-consuming) interactive measurements on the same CLSM images showed that the results were well correlated (liver, r = 1.00; breast, r = 0.92). CONCLUSIONS: Automatic 3D CLSM image cytometry enables measurement of volume and DNA content of large numbers of nuclei in thick histological sections within an acceptable time. This makes large-scale studies feasible, whereby the advantages of CLSM can be exploited fully. The intuitive modular segmentation algorithm presented in this study detects and separates overlapping objects, also in two-dimensional (2D) space. Therefore, this algorithm may also be suitable for other applications.     

Influence of staining on fast automated cell segmentation, feature extraction and cell image analysis

FAZYTAN, a system for fast automated cell segmentation, cell image analysis and extraction of nuclear features, was used to analyze cervical cell images variously stained by the conventional Papanicolaou stain, the new Papanicolaou stain and hematoxylin and thionin only; the last two dyes are used as the nuclear stains in the two versions of the Papanicolaou stain. Other dyes were also tried in cell classification experiments. All cell images in the variously stained samples could be described by the same nuclear features as had been adapted for the discrimination of conventional-Papanicolaou-stained cells. Variances were lower for thionin-stained cells as compared with hematoxylin-stained cells. By application of spectrophotometry, it was confirmed that the spectra of the cytoplasmic counterstains are superimposed on those of the nuclear stains. It appears that a variety of dyes are suitable as cytologic stains for cell classification by the FAZYTAN system, provided that they achieve sufficiently strong nuclear-cytoplasmic contrast by precisely delineating the chromatin texture.