A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.     

An evaluation of semiautomatic approaches to contour segmentation applied to fungal hyphae

Semiautomatic image analysis techniques are particularly useful in biological applications, which commonly generate very complex images, and offer considerable flexibility. However, systematic study of such methods is lacking; most research develops fully automatic algorithms. This paper describes a study to evaluate several different semiautomatic or computer-assisted approaches to contour segmentation within the context of segmenting degraded images of fungal hyphae. Four different types of contour segmentation method, with varying degrees and types of user input, are outlined and applied to hyphal images. The methods are evaluated both quantitatively and qualitatively by comparing results obtained by several test subjects segmenting simulated images qualitatively similar to the hyphal images of interest. An active contour model approach, using control points, emerges as the method to be preferred to three more traditional approaches. Feedback from the image provider indicates that any of the methods described have something useful to offer for segmentation of hyphae.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.     

Segmentation of cell nuclei in tissue section analysis

Image segmentation is a critical step in digital picture analysis, especially for that of tissue sections. As the morphology of the cell nuclei provides important biological information, their segmentation is of particular interest. The known segmentation methods are not adequate for segmenting cell nuclei of tissue sections; the reason for this lies in the optical properties of their images. We have developed new gradient methods of segmentation of previously presegmented images by taking these properties into account and by using the approximately circular shape of the cell nuclei as a priori information. In our first technique, the segment method, the images of the nuclei are divided into eight segments, special gradient filters being defined for each segment. This has enabled us to improve the gradient image. After searching for local maxima, the contours of nuclei can be found. In the second method, the method of transformation into the polar coordinate system (PCS), the a priori information serves to define a circular direction field for gradient computation and contour finding. In contrast with the first method, which offers a rapid, general idea about the nuclear shape, the PCS method permits precise segmentation and morphological analysis of the cell nuclei.     

Multi-Atlas Based Adaptive Active Contour Model with Application to Organs at Risk Segmentation in Brain MR Images

BackgroundAccurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images.MethodsThe proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour.ResultsExperimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy.ConclusionsThe geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.     

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images.The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models.This new function is a combination of the gray-level information and first-order statistical features,called standard deviation parameters.In a comprehensive study,the developed algorithm and the efficiency of segmentation were first tested for synthetic images.Tests were also performed on breast and liver ultrasound images.The proposed method was compared with the watershed approach to show its efficiency.The performance of the segmentation was estimated using the area error rate.Using the standard deviation textural feature and a 5×5 kernel,our curve evolution was able to produce results close to the minimal area error rate(namely 8.88% for breast images and 10.82% for liver images).The image resolution was evaluated using the contrast-to-gradient method.The experiments showed promising segmentation results.     

Analysis of gene expression levels in individual bacterial cells without image segmentation

Studies of stochasticity in gene expression typically make use of fluorescent protein reporters, which permit the measurement of expression levels within individual cells by fluorescence microscopy. Analysis of such microscopy images is almost invariably based on a segmentation algorithm, where the image of a cell or cluster is analyzed mathematically to delineate individual cell boundaries. However segmentation can be ineffective for studying bacterial cells or clusters, especially at lower magnification, where outlines of individual cells are poorly resolved. Here we demonstrate an alternative method for analyzing such images without segmentation. The method employs a comparison between the pixel brightness in phase contrast vs fluorescence microscopy images. By fitting the correlation between phase contrast and fluorescence intensity to a physical model, we obtain well-defined estimates for the different levels of gene expression that are present in the cell or cluster. The method reveals the boundaries of the individual cells, even if the source images lack the resolution to show these boundaries clearly.     

Segmentation of Uterus Using Laparoscopic Ultrasound by an Image-Based Active Contour Approach for Guiding Gynecological Diagnosis and Surgery

In laparoscopic gynecologic surgery, ultrasound has been typically implemented to diagnose urological and gynecological conditions. We applied laparoscopic ultrasonography (using Esaote 7.5~10MHz laparoscopic transducer) on the retrospective analyses of 42 women subjects during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. The objective of our research is to develop robust segmentation technique for isolation and identification of the uterus from the ultrasound images, so as to assess, locate and guide in removing the lesions during laparoscopic operations. Our method enables segmentation of the uterus by the active contour algorithm. We evaluated 42 in-vivo laparoscopic images acquired from the 42 patients (age 39.1 ± 7.2 years old) and selected images pertaining to 4 cases of congenital uterine malformations and 2 cases of pelvic adhesions masses. These cases (n = 6) were used for our uterus segmentation experiments. Based on them, the active contour method was compared with the manual segmentation method by a medical expert using linear regression and the Bland-Altman analysis (used to measure the correlation and the agreement). Then, the Dice and Jaccard indices are computed for measuring the similarity of uterus segmented between computational and manual methods. Good correlation was achieved whereby 84%–92% results fall within the 95% confidence interval in the Student t-test) and we demonstrate that the proposed segmentation method of uterus using laparoscopic images is effective.     

Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method

Automatic image segmentation of immunohistologically stained breast tissue sections helps pathologists to discover the cancer disease earlier. The detection of the real number of cancer nuclei in the image is a very tedious and time consuming task. Segmentation of cancer nuclei, especially touching nuclei, presents many difficulties to separate them by traditional segmentation algorithms. This paper presents a new automatic scheme to perform both classification of breast stained nuclei and segmentation of touching nuclei in order to get the total number of cancer nuclei in each class. Firstly, a modified geometric active contour model is used for multiple contour detection of positive and negative nuclear staining in the microscopic image. Secondly, a touching nuclei method based on watershed algorithm and concave vertex graph is proposed to perform accurate quantification of the different stains. Finally, benign nuclei are identified by their morphological features and they are removed automatically from the segmented image for positive cancer nuclei assessment. The proposed classification and segmentation schemes are tested on two datasets of breast cancer cell images containing different level of malignancy. The experimental results show the superiority of the proposed methods when compared with other existing classification and segmentation methods. On the complete image database, the segmentation accuracy in term of cancer nuclei number is over than 97%, reaching an improvement of 3–4% over earlier methods.     

Biological Image Segmentation Using Region-Scalable Fitting Energy with B-Spline Level Set Implementation and Watershed

ObjectivesImage segmentation plays an important role in the analysis and understanding of the cellular process. However, this task becomes difficult when there is intensity inhomogeneity between regions, and it is more challenging in the presence of the noise and clustered cells. The goal of the paper is propose an image segmentation framework that tackles the above cited problems.Material and methodsA new method composed of two steps is proposed: First, segment the image using B-spline level set with Region-Scalable Fitting (RSF) active contour model, second apply the Watershed algorithm based on new object markers to refine the segmentation and separate clustered cells. The major contributions of the paper are: 1) Use of a continuous formulation of the level set in the B-spline basis, 2) Develop the energy function and its derivative by introducing the RSF model to deal with intensity inhomogeneity, 3) For the Watershed, propose a relevant choice of markers that considers the cell properties.ResultsExperimental results are performed on widely used synthetic images, in addition to simulated and real biological images, without and with additive noise. They attest the high quality of segmentation of the proposed method in terms of quantitative and qualitative evaluation.ConclusionThe proposed method is able to tackle many difficulties at the same time: overlapped intensities, noise, different cell sizes and clustered cells. It provides an efficient tool for image segmentation especially biological ones.     

Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific biomarker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining different automated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert.     

An Automated Three-Dimensional Detection and Segmentation Method for Touching Cells by Integrating Concave Points Clustering and Random Walker Algorithm

Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations'' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.     

河南省封丘农田杂草类型的初步研究

采用聚类分析的方法,研究阐明在气候差异不明显的冲积平原区,农田杂草类型和相应的土壤类型(相当于土属)有明显的相关性。在划分旱地农田杂草类型时,环境条件的差异比栽培作物具有更重要的指示意义。由于杂草多为广布种,采用聚类分析的方法,比单纯用优势种能够更客观地划分杂草类型。 根据实地观测,指出了封丘地区危害严重的主要杂草种类和危害时间,以及不同环境条件下杂草的分布和危害程度,这对于农业生产上杂草的防除工作有一定指导意义。     

Effect of segmentation errors on 3D-to-2D registration of implant models in X-ray images

In many biomedical applications, it is desirable to estimate the three-dimensional (3D) position and orientation (pose) of a metallic rigid object (such as a knee or hip implant) from its projection in a two-dimensional (2D) X-ray image. If the geometry of the object is known, as well as the details of the image formation process, then the pose of the object with respect to the sensor can be determined. A common method for 3D-to-2D registration is to first segment the silhouette contour from the X-ray image; that is, identify all points in the image that belong to the 2D silhouette and not to the background. This segmentation step is then followed by a search for the 3D pose that will best match the observed contour with a predicted contour. Although the silhouette of a metallic object is often clearly visible in an X-ray image, adjacent tissue and occlusions can make the exact location of the silhouette contour difficult to determine in places. Occlusion can occur when another object (such as another implant component) partially blocks the view of the object of interest. In this paper, we argue that common methods for segmentation can produce errors in the location of the 2D contour, and hence errors in the resulting 3D estimate of the pose. We show, on a typical fluoroscopy image of a knee implant component, that interactive and automatic methods for segmentation result in segmented contours that vary significantly. We show how the variability in the 2D contours (quantified by two different metrics) corresponds to variability in the 3D poses. Finally, we illustrate how traditional segmentation methods can fail completely in the (not uncommon) cases of images with occlusion.     

基于超声心动图的二尖瓣轮廓提取

针对超声心动图噪音大,灰阶少等弱点,采用多阈值的门限法对图象进行正确分割,在通过跟踪特征点进行匹配的基础上,采用匹配后插值的方法,提高了匹配的精度。并利用前一帧的速度解决了粘连在一起的二尖瓣轮廓线的分割问题。实验取得了较满意的结果。     

Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.     

Applications of binary segmentation to the estimation of quantal response curves and spatial intensity

This paper explores the use of binary segmentation procedures in two applications. The first application is concerned with the estimation of nonparametric quantal response curves. With Bernoulli data and an assumed monotone increasing curve, this gives rise a change-point model where the change points are determined using a sequence of nested hypothesis tests of whether a change point exists. The second application concerns cluster identification and inference for spatial data where the shape of the clusters and the number of clusters is unknown. The procedure involves a sequence of nested hypothesis tests of a single cluster versus a pair of distinct clusters. Examples of both applications are provided.     

Sensitivity of MRI signal distribution within the intervertebral disc to image segmentation and data normalisation

There is a lack of early biomarkers of intervertebral disc (IVD) degeneration. Thus, the authors developed the analysis of magnetic resonance signal intensity distribution (AMRSID) method to analyse the 3D distribution of the T2-weighted MR signal intensity within the IVD using normalised histograms, weighted centres and volume ratios. The objective was to assess the sensitivity of the AMRSID method to the segmentation process and data normalisation. Repetition of the semi-automatic segmentation by the same operator did not influence the quality of the contour or our new MR distribution parameters whereas the skills of the operator influenced only the MR distribution parameters, and the instructions given prior to the segmentation influenced both the quality of the contour and the MR distribution parameters. Bone normalisation produces an index that jointly highlights IVD and bone health, whereas cerebrospinal fluid normalisation only suppresses the effect of the acquisition gain. This robust AMRSID method has the potential to improve the diagnostic with earlier biomarkers and the prognosis of evolution.     

Biview Learning for Human Posture Segmentation from 3D Points Cloud

Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation.