Watershed Technique Research Articles

Medical Image Segmentation Based on Edge Detection Techniques

In this article a new combination of image segmentation techniques including K-means clustering, watershed transform, region merging and growing algorithm was proposed to segment computed tomography(CT) and magnetic resonance(MR) medical images. The first stage in the proposed system is preprocessing for required image enhancement, cropped, and convert the images into .mat or png ...etc image file formats then the image will be segmented using combination methods (clustering , region growing, and watershed, thresholding). Some initial over-segmentation appears due to the high sensitivity of the watershed algorithm to the gradient image intensity variations. Here, K- means and region growing with correct thresholding value are used to overcome that over segmentations. in our system the number of pixels of segmented area is calculated which is very important for medical image analysis for diseases or medicine effects on affected area of human body. also displaying the edge map. The results show that using clustering method output to region growing as input image, gives accurate and very good results compare with watershed technique which depends on gradient of input image, the mean and the threshold values which are chosen manually. Also the results show that the manual selection of the threshold value for the watershed is not as good as automatically selecting, where data misses may be happen.

An advanced image analysis tool for the quantification and characterization of breast cancer in microscopy images.

The paper presents an advanced image analysis tool for the accurate and fast characterization and quantification of cancer and apoptotic cells in microscopy images. The proposed tool utilizes adaptive thresholding and a Support Vector Machines classifier. The segmentation results are enhanced through a Majority Voting and a Watershed technique, while an object labeling algorithm has been developed for the fast and accurate validation of the recognized cells. Expert pathologists evaluated the tool and the reported results are satisfying and reproducible.

连栽第1和第2代杉木近熟林水文学过程中养分动态比较

PDF HTML阅读 XML下载 导出引用 引用提醒 连栽第1代和第2代杉木近熟林水文过程养分动态比较 DOI: 10.5846/stxb201308092050 作者: 作者单位: 国家野外科学观测研究站;中南林业科技大学,中南林业科技大学,中南林业科技大学,中南林业科技大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家林业公益性行业科研专项(201104009);科技部公益性研究项目(2007-4-15);国家野外科学观测研究站项目(20080615);湖南省教育厅重点项目(2011A135) Characteristics of nutrient dynamic in hydrological processes of first and second rotations of prematured Chinese fir plantations Author: Affiliation: National Field Station for Scientific Observation ＆ Experiment in Huitong Hunan Huitong;Central South University of Forestry and Technology,Central South University of Forestry and Technology,Central South University of Forestry and Technology,Central South University of Forestry and Technology Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用小集水区径流场技术和定位研究方法, 在获得连栽两代杉木近熟林的大气降水、穿透林冠水、地表、地下径流量等水文学数据,并测定其养分含量的基础上,研究了连栽两代杉木近熟林水文过程的营养动态特征。结果表明:降水中养分浓度第2 代林比第1 代林高20.30%-39.64%,养分的输入量比第1 代多38.52%;穿透水中养分浓度,第1 代和第2 代林分别比大气降水中高4.149-4.895 g/kg和4.271-5.605g/kg,雨水对冠层营养物质的淋溶,第2 代比第1 代高2.94%-21.37%;地表径流中的养分浓度两代林差异不大,地下径流中的养分浓度第2 代林比第1 代高48.06%-78.87%,径流输出的养分量第2 代林是第1 代林的1.58-2.61倍;养分地球化学循环中,第1 代林养分地球化学循环速率26.75%-29.95%,第2 代林37.24%-47.43%,养分地球化学循环的周期第1 代林3.3-3.7a, 第2 代林2.1-2.7a,养分地球化学循环中第2 代林的养分流失率是第1 代林的1.30-1.72倍,养分的净积累率只有第1 代林的73.57%-87.14%。系统持留与利用由外界输入的养分功能上,第2 代林低于第1 代林。 Abstract:Based on the small watershed technique and the located observation method, the hydrology data collected of the precipitation, penetrate the canopy water, surface and underground runoff, and determine its nutrient content, which were used for analyzing the nutrient dynamic in hydrologic process on the successive rotations of Chinese fir plantations at near mature forest. The replanting its purpose to reveal functional differences in the two generations of Chinese fir in biogeochemical cycling of nutrients, and study the problem of declining productivity even planted fir plantations provide ideas and evidence. The results showed that the nutrient concentrations in precipitation of the second generation forest was 20.30%-39.64% higher than that in first-generation forest, which accounted in total for 38.52% more input of nutrients in second generation than that in the first generation forest. The nutrient concentrations in the through fall in the forest of the first and the second generation was relatively 4.149-4.895 g/kg and 4.271-5.605 g/kg higher than that in the corresponding atmospheric precipitation, respectively. Rain canopy leaching of nutrients,which the nutrient content was 2.94% to 21.37% higher in the second generation forest than that in the first generation forest after leaching through the canopy. There was not significantly different between the nutrient concentrations in surface runoff of the first and that in the second generation forest. The nutrient concentrations subsurface runoff of second generation forest, however, was 48.06% -78.87% higher than that in the first generation forest, which induced the runoff output in the runoff, the second generation forest was 1.58 to 2.61 times more than that in the first generation forest. The nutrient geochemical rate was 26.75% to 29.95% in the first generation forest, while in the second generation forest the nutrient geochemical cycle was 37.24% to 47.43%, and The nutrient geochemical cycle was 3.3 to 3.7 years in the first generation forest, while was 2.1 to 2.7 years in the second generation forest. The nutrient loss rate of the second generation forest was from 1.30 to 1.72 times higher than that in the first generation forest, and the nutrient accumulation rate in the second generation forest was from 73.57% to 87.14% of that in the first generation forest. As to the retention and utilization in the input of nutrients from the outside, the nutrient retention and utilization efficiency of the second generation forest was lower than that of the first generation forest. 参考文献 相似文献 引证文献

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification.

Brain Tumor Segmentation with Wavelet Watershed and Detection Using Multi-SVM Classifier

Detection of tumour in brain has most prominence in the recent years. Various processes are proposed for detecting BRAIN TUMOUR which comprises with image segmentation and classification process. But classification process has dominant and suppressed most of the techniques by its advantages of detecting and classifying brain tumour. In this paper a novel approach of Wavelet watershed technique is proposed with MULTI RBF SVM classifier process for segmentation and classification processes respectively. The feature extraction and region segmentation processes were completed by Wavelet Watershed technique for this we used to calculate the energy of the image for a texture level classification mode. Under Multi SVM classifier the weight comes into play for training datasets along with classification mode. Experimental results are acquired from the proposed technique is about 95%.

Universal density profile for cosmic voids.

We present a simple empirical function for the average density profile of cosmic voids, identified via the watershed technique in ΛCDM N-body simulations. This function is universal across void size and redshift, accurately describing a large radial range of scales around void centers with only two free parameters. In analogy to halo density profiles, these parameters describe the scale radius and the central density of voids. While we initially start with a more general four-parameter model, we find two of its parameters to be redundant, as they follow linear trends with the scale radius in two distinct regimes of the void sample, separated by its compensation scale. Assuming linear theory, we derive an analytic formula for the velocity profile of voids and find an excellent agreement with the numerical data as well. In our companion paper [Sutter et al., arXiv:1309.5087 [Mon. Not. R. Astron. Soc. (to be published)]], the presented density profile is shown to be universal even across tracer type, properly describing voids defined in halo and galaxy distributions of varying sparsity, allowing us to relate various void populations by simple rescalings. This provides a powerful framework to match theory and simulations with observational data, opening up promising perspectives to constrain competing models of cosmology and gravity.

Mathematical morphology for TOFD image analysis and automatic crack detection

The aim of this work is to automate the interpretation of ultrasonic images during the non-destructive testing (NDT) technique called time-of-flight diffraction (TOFD) to aid in decision making.In this paper, the mathematical morphology approach is used to extract relevant pixels corresponding to the presence of a discontinuity, and a pattern recognition technique is used to characterize the discontinuity. The watershed technique is exploited to determine the region of interest and image background is removed using an erosion process, thereby improving the detection of connected shapes present in the image. Remaining shapes, are finally reduced to curves using a skeletonization technique. In the case of crack defects, the curve formed by such pixels has a parabolic form that can be automatically detected using the randomized Hough transform.

An integrated GIS tool for automatic forest inventory estimates of Pinus radiata from LiDAR data

This article presents an integrated GIS tool for automatic forest inventory of Pinus radiata plantations from light detection and ranging (LiDAR) data. Built as a set of tools running in the desktop GIS software package ArcGIS, it integrates spatial analysis, LiDAR data analysis and image segmentation techniques as well as empirical tree models to support forest inventories of Pinus radiata on an individual-tree basis. The integrated GIS tool allows users to define or select plots to extract LiDAR data for forest inventory, build and process canopy height models (CHMs) from the extracted LiDAR data through surface modelling, delineate individual trees on the CHMs by applying the marker-controlled watershed segmentation technique, and to derive forest inventory estimates based on the CHMs and identified individual trees through spatial analysis and tree modelling using the empirical models. It takes advantage of combining GIS and LiDAR to automatically conduct a forest inventory, build and manage a forest inventory database and to spatially and statistically summarise and visualise the inventory data. Although developed for forest inventories of Pinus radiata plantations in Victoria, Australia, the integrated tool can be customised for forest inventories of Pinus radiata plantations in other regions and for other types of plantations by incorporating empirical tree models built and calibrated for those regions and those species of trees.

Assessment of leachate effects to the drinking water supply units in the down slope regions of municipal solid waste (MSW) dumping sites in Lahore Pakistan

The aim of this study was to evaluate seasonal as well as moderate temporal effects on deep groundwater quality by the dumpsites leachate in the Lahore Metropolitan with a population of about 8 million. Groundwater samples for this study ware drawn from groundwater supply units installed by local government for the provision of drinking water to the residents. The groundwater in down slope regions from municipal solid waste (MSW) dumping sites has been identified by using Inverted Watershed technique. Eleven water supply units have been spotted that fall within the identified leachate plumes. Parameters of pH, turbidity, electric conductivity, total dissolved solids (TDS) and Escherichia coli were determined on water samples from these identified units. The quality analysis indicates that the effect of the leachate is more prominent in the hot and dry pre-monsoon season due to the domination of base flow for recharge of the groundwater, whereas dilution observed in leachate contamination during and immediately after the monsoon rainy season is due to the recharge through seepage of the rain water which dominates the base flow. Variation in the correlation factor between electric conductivity and TDS has been used to estimate constituents of TDS contributed both by rainfall seepage and base flow. An increasing tendency, showing influence of the leachate on the deep water quality, has been observed while analyzing the correlation between distance of the tube wells from leachate sources and measurements of quality parameters. Key words: Base flow, deep groundwater, down slope regions, landfill leachate, rainwater seepage.

A HYBRID APPROACH FOR INFORMATION EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY

This paper presents a hybrid approach for extraction of information from high resolution satellite imagery and also demonstrates the accuracy achieved by the final extracted information. The hybrid technique comprises of improved marker-controlled watershed transforms and a nonlinear derivative method. It overcomes all the disadvantages of existing region-based and edge-based methods by incorporating aforesaid hybrid methods. It preserves the advantages of multi-resolution and multi-scale gradient approaches. Region-based segmentation also incorporates the watershed technique due to its better efficiency in segmentation. In principle, a proper segmentation can be performed perfectly by watershed technique on incorporating ridges. These ridges express as the object's boundaries according to the property of contour detection. On the other hand, the nonlinear derivative method is used for resolving the discrete edge detection problem. Since it automatically selects the best edge localization, which is very much useful for estimation of gradient selection. The main benefit of univocal edge localization is to provide a better direction estimation of the gradient, which helps in producing a confident edge reference map for synthetic images. The practical merit of this proposed method is to derive an impervious surface from emerging urban areas.

Lung tumor segmentation in PET images using graph cuts

The aim of segmentation of tumor regions in positron emission tomography (PET) is to provide more accurate measurements of tumor size and extension into adjacent structures, than is possible with visual assessment alone and hence improve patient management decisions. We propose a segmentation energy function for the graph cuts technique to improve lung tumor segmentation with PET. Our segmentation energy is based on an analysis of the tumor voxels in PET images combined with a standardized uptake value (SUV) cost function and a monotonic downhill SUV feature. The monotonic downhill feature avoids segmentation leakage into surrounding tissues with similar or higher PET tracer uptake than the tumor and the SUV cost function improves the boundary definition and also addresses situations where the lung tumor is heterogeneous. We evaluated the method in 42 clinical PET volumes from patients with non-small cell lung cancer (NSCLC). Our method improves segmentation and performs better than region growing approaches, the watershed technique, fuzzy-c-means, region-based active contour and tumor customized downhill.

Medical Evaluation of Marker Controlled Watershed Technique for Detecting Brain Tumors

Segmentation has a vital role in the field of Medical Science especially in the area of cancer research for detecting tumors. Proper diagnosis and effective treatment is only possible if we have an efficient segmenting technique. In this paper we present a sound segmentation technique named as marker controlled watershed technique which is based on repeated morphological image dilation and erosion operations to carry out segmentation. After segmenting the desired area we evaluate this technique for its accuracy and performance. KeywordsMethod, ROI, Segmentation, Brain Tumor, Image

Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images

BackgroundFor some scoliotic patients the spinal instrumentation is inevitable. Among these patients, those with stiff curvature will need thoracoscopic disk resection. The removal of the intervertebral disk with only thoracoscopic images is a tedious and challenging task for the surgeon. With computer aided surgery and 3D visualisation of the interverterbral disk during surgery, surgeons will have access to additional information such as the remaining disk tissue or the distance of surgical tools from critical anatomical structures like the aorta or spinal canal. We hypothesized that automatically extracting 3D information of the intervertebral disk from MR images would aid the surgeons to evaluate the remaining disk and would add a security factor to the patient during thoracoscopic disk resection.MethodsThis paper presents a quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images. The automatic segmentation method is based on the watershed technique and morphological operators. The 3D Dice Similarity Coefficient (DSC) is the main statistical metric used to validate the automatically detected preoperative disk volumes. The automatic detections of intervertebral disks of real clinical MR images are compared to manual segmentation done by clinicians.ResultsResults show that depending on the type of MR acquisition sequence, the 3D DSC can be as high as 0.79 (±0.04). These 3D results are also supported by a 2D quantitative evaluation as well as by robustness and variability evaluations. The mean discrepancy (in 2D) between the manual and automatic segmentations for regions around the spinal canal is of 1.8 (±0.8) mm. The robustness study shows that among the five factors evaluated, only the type of MRI acquisition sequence can affect the segmentation results. Finally, the variability of the automatic segmentation method is lower than the variability associated with manual segmentation performed by different physicians.ConclusionsThis comprehensive evaluation of the automatic segmentation and 3D reconstruction of intervertebral disks shows that the proposed technique used with specific MRI acquisition protocol can detect intervertebral disk of scoliotic patient. The newly developed technique is promising for clinical context and can eventually help surgeons during thoracoscopic intervertebral disk resection.

Phase Symmetry Approach Applied to Children Heart Chambers Segmentation: A Comparative Study

Segmentation of echocardiographic images presents a great challenge because these images contain strong speckle noise and artifacts. Besides, most ultrasound segmentation methods are semi-automatic, requiring initial contour to be manually identified in the images. In this work, we propose an algorithm based on the phase symmetry approach and level set evolution, in order to extract simultaneously all heart cavities in a fully automatic way. The level set evolution uses a new logarithmic based stopping function, which demonstrates to perform well in the boundary extraction. We compared our method with other level set approaches, the watershed technique, and the manual segmentation made by two physicians. The experimental work was based on echocardiography images of children. Similarity metrics, namely Pratt Function, Pixel Mean Error, and Similarity Angle have been used for the performance evaluation of the different methods. The results indicate that our method has a performance at least 4% superior to the other methods able to segment the four chambers. Even for the two worst boundary extraction cases (right ventricle and left atrium) the performance of the proposed method still is better than the other techniques.

Watershed segmentation of basal left ventricle for quantitation of cine cardiac MRI function

To quantitatively analyze global and regional cardiac function from MR, clinical parameters such as ejection fraction (EF) and volumes are required. These depend upon accurate delineation of endo- and epicardial contours of the left ventricle (LV). Previous work [1] has demonstrated the difficulty of accurate LV segmentation, especially in basal slices where the LV outflow tract (LVOT) interrupts continuous myocardial contours. A novel method for the robust, accurate and fully automatic LV segmentation from short axis (SA) cine MR images is presented in this study that applies watershed technique to solve basal slice segmentation.

Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images

In previous research, a watershed-based algorithm was shown to be useful for automatic lesion segmentation in dermoscopy images, and was tested on a set of 100 benign and malignant melanoma images with the average of three sets of dermatologist-drawn borders used as the ground truth, resulting in an overall error of 15.98%. In this study, to reduce the border detection errors, a neural network classifier was utilized to improve the first-pass watershed segmentation; a novel “edge object value (EOV) threshold” method was used to remove large light blobs near the lesion boundary; and a noise removal procedure was applied to reduce the peninsula-shaped false-positive areas. As a result, an overall error of 11.09% was achieved.

Watershed segmentation of dermoscopy images using a watershed technique

Automatic lesion segmentation is an important part of computer-based image analysis of pigmented skin lesions. In this research, a watershed algorithm is developed and investigated for adequacy of skin lesion segmentation in dermoscopy images. Hair, black border and vignette removal methods are introduced as preprocessing steps. The flooding variant of the watershed segmentation algorithm was implemented with novel features adapted to this domain. An outer bounding box, determined by a difference function derived from horizontal and vertical projection functions, is added to estimate the lesion area, and the lesion area error is reduced by a linear estimation function. As a post-processing step, a second-order B-Spline smoothing method is introduced to smooth the watershed border. Using the average of three sets of dermatologist-drawn borders as the ground truth, an overall error of 15.98% was obtained using the watershed technique. The implementation of the flooding variant of the watershed algorithm presented here allows satisfactory automatic segmentation of pigmented skin lesions.

An Overview of the Topological Gradient Approach in Image Processing: Advantages and Inconveniences

Image analysis by topological gradient approach is a technique based upon the historic application of the topological asymptotic expansion to crack localization problem from boundary measurements. This paper aims at reviewing this methodology through various applications in image processing; in particular image restoration with edge detection, classification and segmentation problems for both grey level and color images is presented in this work. The numerical experiments show the efficiency of the topological gradient approach for modelling and solving different image analysis problems. However, the topological gradient approach presents a major drawback: the identified edges are not connected and then the results obtained particularly for the segmentation problem can be degraded. To overcome this inconvenience, we propose an alternative solution by combining the topological gradient approach with the watershed technique. The numerical results obtained using the coupled method are very interesting.

GIS-based evaluation of micro-watersheds to ascertain site suitability for water conservation structures

The planning of conservation measures to conserve water and soil resources taking hydrological planning unit as micro-watershed is considered to be effective. The automated watershed delineation technique using the spline interpolated filled digital elevation model (DEM) is effective in converging slopes of the area in which the stream patterns match with the manually digitized stream patterns of the topographical map. The various vector spatial layers like the slope/aspect, land-use/land-cover, runoff potential, soil erosion potential and the associated attribute information governing the criteria for different conservation structures can act as input layers in integrated spatial analysis module in GIS environment to evolve derived layers indicating the locations of conservation sites meeting the requisite criteria. The reliability of suitable conservation sites suggested out of integrated spatial GIS analysis could be ascertained using the multi criteria analysis incorporating the various factors controlling soil erosion process in the micro-watershed groups. The details of the above work are discussed in the paper.

Object density-based image segmentation and its applications in biomedical image analysis

In many applications of medical image analysis, the density of an object is the most important feature for isolating an area of interest (image segmentation). In this research, an object density-based image segmentation methodology is developed, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The proposed method consists of three main stages: preprocessing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are several subtasks of preprocessing. Object segmentation utilizes a marker-controlled watershed technique to identify each object of interest (OI) from the background. A marker estimation method is proposed to minimize over-segmentation resulting from the watershed algorithm. Object segmentation provides an accurate density estimation of OI which is used to guide the subsequent segmentation steps. The final stage converts the distribution of OI into textural energy by using fractal dimension analysis. An energy-driven active contour procedure is designed to delineate the area with desired object density. Experimental results show that the proposed method is 98% accurate in segmenting synthetic images. Segmentation of microscopic images and ultrasound images shows the potential utility of the proposed method in different applications of medical image processing.