Watershed Algorithm Research Articles

Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

Image Segmentation Algorithm Combined with Regularized P-M De-Noising Model and Improved Watershed Algorithm

Aiming at the problems of noise sensitivity and unclear contour in existing MRI image segmentation algorithms, a segmentation method combining regularized P-M de-noising model and improved watershed algorithm is proposed. First, the brain MRI image is pre-processed to obtain a brain nuclear image. Then, the brain nuclear image is de-noised by a regularized P-M model. After that, the image is preliminarily segmented by the traditional watershed algorithm to extract the features of each small region. Finally, the small regions are merged by Fuzzy Clustering with Spatial Pattern (FCSP) to obtain the segmentation image with smooth edges. The experimental results show that the algorithm can accurately segment the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. The average AOM and ME of the segmentation results on the BrainWeb dataset reached 0.93 and 0.04, respectively.

Artificial Intelligence Identification of Multiple Microfossils from the Cambrian Kuanchuanpu Formation in Southern Shaanxi, China

AbstractThe Cambrian Kuanchuanpu Formation in southern Shaanxi, China is a critical window for the understanding of the Cambrian explosion, because of abundant and various exceptionally preserved metazoans and embryo fossils yielded. The efficiency of traditional sample manually selecting with microscopes is quite low and hinder the discoveries of new species, thus recognition and classification of microfossils by artificial intelligence (AI) is substantially in the request. In this paper, we develop a procedure for fossil area segmentation in common multi‐typed mixed photos by improved watershed algorithm. And for better fossil recognition, previous histogram of oriented grandient (HOG) algorithm is replaced by scale invariant feature transform (SIFT), which is feasible for the segmented images and increase the accuracy significantly. Thus, the scope of application of AI fossil recognition can be extended form single fossil image to multi‐typed mixed images and the reliability is also secured, as the result of our test presents a high (at least 84%) accuracy of fossil recognition.

Spatial-spectral identification of abnormal leukocytes based on microscopic hyperspectral imaging technology

Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia (ALL). As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution, which significantly affects the absorption and reflection of light, the spectral feature is proved to be important for leukocytes classification and identification. This paper proposes an accurate identification method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging (HSI) technology which combines the spectral information. The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm. Then, the spectral features are extracted and combined with the spatial features. Based on this, the support vector machine (SVM) is applied for classification of five types of leukocytes and abnormal leukocytes. Compared with different classification methods, the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes, improving the accuracy in the classification and identification of leukocytes. This paper only selects one subtype of ALL for test, and the proposed method can be applied for detection of other leukemia in the future.

Overlapping Cervical Cell Image Segmentation Based on Bottleneck Detection and Watershed Algorithm

This study proposes an image segmentation method based on bottleneck detection and watershed algorithm to solve the problem of overlapping cervical cell image. First, we use polygon approximation to get all feature points on the cell contour and then use bottleneck detection and ellipse fitting to obtain the correct split point pairs. Therefore, the approximate range of the overlapping region was determined. The watershed algorithm was used to obtain the internal boundary information for the gradient image of the region. Finally, the segmentation results of the overlapped cells were obtained by superimposing with the outer contour. The experimental results show that this algorithm can segment the contour of a single cell from the overlapping cervical cell images with good accuracy and integrity. The segmentation result is close to that of doctors' manual marking, and the segmentation result is better than other existing algorithms.

An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds

Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from 3-D information loss due to the interpolation operation. Therefore, this article proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3-D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the principal component analysis defined local coordinate reference framework, a multidirectional 3-D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.

Automatic segmentation and recognition of red and white cells in stool microscopic images of human

Aiming to solve the problem of low efficiency in manually recognizing the red and white cells in stool microscopic images, we propose an automatic segmentation method based on iterative corrosion with marker-controlled watershed segmentation and an automatic recognition method based on support vector machine (SVM) classification. The method first obtains saliency map of the images in HSI and Lab color spaces through saliency detection algorithm, then fuses the salient images to complete the initial segmentation. Next, we segment the red and white cells completely based on the initial segmentation images using marker-controlled watershed algorithm and other complementary methods. According to the differences in geometrical and texture features of red and white cells such as area, perimeter, circularity, energy, entropy, correlation and contrast, we extract them as feature vectors to train SVM and finally complete the classification and recognition of red and white cells. The experimental results indicate that our proposed marker-controlled watershed method can help increase the segmentation and recognition accuracy. Moreover, since it is also less susceptible to the heteromorphic red and white cells, our method is effective and robust.

Image Segmentation for the Treatment Planning of Magnetic Resonance-Guided High-Intensity Focused Ultrasound (MRgHIFU) Therapy: A Parametric Study

In the present research work, image segmentation methods were studied to find internal parameters that provide an efficient identification of the regions of interest in Magnetic Resonance (MR) images used for the therapy planning of High-Intensity Focused Ultrasound (HIFU), a minimally invasive therapeutic method used for selective ablation of tissue. The involved image segmentation methods were threshold, level set and watershed segmentation algorithm with markers (WSAM), and they were applied to transverse and sagittal MR images obtained from an experimental setup of a murine experiment. A parametric study, involving segmentation tests with different values for the internal parameters, was carried out. The F-measure results from the parametric study were analyzed by region using Welch’s ANOVA followed by post hoc Games-Howell test to determine the most appropriate method for region identification. In transverse images, the threshold method had the best performance for the air region with a F-measure median of 0.9802 (0.9743–0.9847, interquartile range IQR 0.0104), the WSAM for the tissue, gel-pad, transducer and water region with a F-measure median of 0.9224 (0.8718–0.9468, IQR 0.075), 0.9553 (0.9496–0.9606, IQR 0.011), 0.9416 (0.9330–0.9540, IQR 0.021) and 0.9769 (0.9741–0.9803, IQR 0.0062), respectively. In sagittal images, threshold method had the best performance for the air region with a F-measure median of 0.9680 (0.9589–0.9735, IQR 0.0146), the WSAM for the tissue and gel-pad regions with a F-measure median of 0.9241 (0.8870–0.9426, IQR 0.0556) and 0.9553 (0.9472–0.9625, IQR 0.0153), respectively, and the Geodesic Active Contours (GAC) method for the transducer and water regions with a F-measure median of 0.9323 (0.9221–0.9402, IQR 0.0181) and 0.9681 (0.9627–0.9715, IQR 0.0088), respectively. The present research work integrates preliminary results to generate more efficient procedures of image segmentation for treatment planning of the MRgHIFU therapy. Future work will address the search of an automatic segmentation process, regardless of the experimental setup.

Study of the formation of tungsten powder by hydrogen reduction of ammonium paratungstate and stereometric analyses of the powder texture

The micromorphology of tungsten particles obtained by means of the annealing and hydrogen reduction of ammonium paratungstate (APT) has been finely analyzed during different formation stages using scanning electron microscopy (SEM). The intermediate tungsten oxides appearing during the different reactions were identified, and their particle morphologies were analyzed. The surface texture and texture isotropy of the final tungsten powder were investigated utilizing different methodologies. The surface segmentation using the watershed segmentation algorithm revealed fractal features.

An Improved Hybrid Segmentation Method for Remote Sensing Images

Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).

Drainage basin delineation for outlet glaciers of Northeast Greenland based on Sentinel-1 ice velocities and TanDEM-X elevations

The drainage divides of ice sheets separate the overall glaciated area into multiple sectors. These drainage basins are essential for partitioning mass changes of the ice sheet, as they specify the area over which basin specific measurements are integrated. The delineation of drainage basins on ice sheets is challenging due to their gentle slopes accompanied by local terrain disturbances and complex patterns of ice movement. Until now, in Greenland the basins have been mostly delineated along the major ice divides, which results in large drainage sectors containing multiple outlet glaciers. However, when focusing on measuring glaciological parameters of individual outlet glaciers, more detailed drainage basin delineations are needed. Here we present for the first time a detailed and fully traceable approach that combines ice sheet wide velocity measurements by Sentinel-1 and the high resolution TanDEM-X global DEM to derive individual glacier drainage basins. We delineated catchments for the Northeast Greenland Ice Sheet with a modified watershed algorithm and present results for 31 drainage basins. Even though validation of drainage basins remains a difficult task, we estimated basin probabilities from Monte-Carlo experiments and applied the method to a variety of different ice velocity and DEM datasets finding discrepancies of up to 16% in the extent of catchment areas. The proposed approach has the potential to produce drainage areas for the entirety of the Greenland and Antarctic ice sheets.

Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed segmentation algorithms

Abstract Decay caused by Penicillium spp. fungi is one of the main problems affecting marketing of citrus fruit after harvest because the fungal infection can spread fast from a small number of decayed fruit to the whole consignment. However, the automatic detection of decayed citrus is still a challenge. Early decay of citrus happen on surface peel and present a obvious symptom of water-soaked with cell tissue collapse, which may offer the feasibility of transmittance imaging mode to detect decayed region of citrus. In this study, image processing methods including principal component analysis (PCA), pseudo-color image transformation technology and improved watershed segmentation algorithms (IWSA) were employed to analyze the feasibility of decay detection based on the scanned hyperspectral transmittance images (325−1098 nm) of sound and decayed oranges. The results show that PC3 image is promising for decay segmentation. G components extracted from pseudo-color images of PC3 were selected to enhance image contrasts between decayed and sound tissues, and then decayed regions were segmented perfectly by IWSA whether the defects located on the edge or center position of oranges. However, stem-end tissue had similar features with decayed tissue and therefore were easily misidentified as decayed tissues for those decayed samples with stem-end tissue, and so stem-end identification was carried out. PC2 image and R components extracted from pseudo-color images of PC2 were promising for stem-end identification, then IWSA and morphological parameters were used to extract stem-end region. The stem-end was marked in both operations of decay segmentation and stem-end identification, hence decayed region were further determined for eliminating the misclassification interference of stem-end tissue on decay detection by removing the stem-end region from the operation of decay segmentation. For a validation set including of 84 decayed and 66 sound fruit, the success rates were 93 % and 96 %, respectively, and 94 % for decayed, sound and all fruit. Hyperspectral transmittance imaging offers a novel method for automatic detection of early decayed orange caused by fungus.

Water leakage image recognition of shield tunnel via learning deep feature representation

With the development of urban metro, the research on structural diseases of shield tunnels has been becoming a hot research topic, especially the leakage water diseases. Deep learning-based algorithms have shown impressive performance in image processing domain, such as image classification, image recognition or image retrieval. In this paper, we propose a novel image recognition algorithm for water leakage diseases of shield tunnels based on deep learning algorithm. Water leakage images are classified into six categories, each of which are extracted deep representation for image recognition. We compare our method with Otsu algorithm (OA), Region Growing Algorithm (RGA), and Watershed Algorithm (WA) to show the effectiveness of our proposed method.

The extraction algorithm of color disease spot image based on Otsu and watershed

In this paper, an extraction algorithm of color disease spot image based on Otsu and watershed is proposed to overcome the problem of excessive segmentation in the traditional watershed algorithm. The proposed algorithm converts the color space of the color spot image to calculate the component gradient that is not interfered by the reflected light in the new color space. Then, final gradient image is obtained from the gradient image reconstructed by open and close operation under the different-sized structural elements. The label from the final gradient image is extracted by Otsu algorithm, and then, the H-minima transform is used to modify the labeled image. The modified gradient image is transformed with a label by the watershed algorithm. Finally, the extraction of the disease spot is implemented. The experimental results show that the proposed approach obtains accurate and continuous target contour and reaches the requirement of human visual characteristics. Compared with other similar algorithms, the proposed algorithm can effectively suppress the impact of reflected light, optimize the extraction results of disease spot, better maintain the information of disease spot image, and improve the robustness and the applicability.

Convolutional Neural Network Based Medical Image Classifier

Deep learning had provided good outcome in analyzing images of tumours, however, the deficiency of large annotated datasets reduces its importance. The proposed medical image processing system is based on image segmentation and image classification. It is to be used by medical field experts. In order to classify the Brain tumour images the semantic level classification and segmentation network techniques are applied. This includes prior knowledge of testing samples and training samples, using Convolutional Neural Networks (CNN). The CNN based classifier improves the detection accuracy compared to the existing segmentation based classifier. In this project, the automated system would help the medical image analyst to identify the Brain Tumour in patient by making use of deep convolutional neural network (CNN).The image is obtained from MRI scan of a brain. The tumourless patient’s image dataset is used as the training and testing data for the classification network. Patient image is compared with dataset of a tumour affected images for differentiating an image as Non-timorous sample, low grade glioma and glioblastoma after segmenting and classifying the image. The Watershed segmentation algorithm is used for segmenting images and CNN is used for classifying the images. Finally the system will detect the tumour is affected or not in the given image of a patient’s brain, then the system will identify the tumour affected region and differentiate the low grade glioma and glioblastoma in the image.

Reconstruction of coal pore network based on improved watershed algorithm and seepage simulation

Reconstruction of coal pore network based on improved watershed algorithm and seepage simulation

Semantic Labeling of natural Scene Images Using Color Features

Scene image classification systems firstly need to locate the objects, and then classify the whole image. The color feature is importance to describe the properties of an image surface. The paper presents a framework for scene images to label local regions using color features. The paper uses maker-controlled watershed algorithm to segment the input image into regions. This paper uses the segmented regions as a basic input unit, and then extract Color Histogram (CH) and Color Moment (CM) features in HSV space. This system performs labeling using 3-layer Feed Forward Neural Network (FFNN) classifier. The system tests accuracy on public Microsoft Research Cambridge (MSRC) 9-class dataset.

Tree crown segmentation based on a tree crown density model derived from Airborne Laser Scanning

ABSTRACTThis letter describes a new algorithm for automatic tree crown delineation based on a model of tree crown density, and its validation. The tree crown density model was first used to create a correlation surface, which was then input to a standard watershed segmentation algorithm for delineation of tree crowns. The use of a model in an early step of the algorithm neatly solves the problem of scale selection. In earlier studies, correlation surfaces have been used for tree crown segmentation, involving modelling tree crowns as solid geometric shapes. The new algorithm applies a density model of tree crowns, which improves the model’s suitability for segmentation of Airborne Laser Scanning (ALS) data because laser returns are located inside tree crowns. The algorithm was validated using data acquired for 36 circular (40 m radius) field plots in southern Sweden. The algorithm detected high proportions of field-measured trees (40–97% of live trees in the 36 field plots: 85% on average). The average proportion of detected basal area (cross-sectional area of tree stems, 1.3 m above ground) was 93% (range: 84–99%). The algorithm was used with discrete return ALS point data, but the computation principle also allows delineation of tree crowns in ALS waveform data.

Cancer Recognition in Medical Image Processing using Watershed Algorithm

Lung tumor is by all accounts the basic reason for death among individuals all through the world. Survival from lung tumor is straightforwardly identified with its development at its discovery time. The prior the identification is, the higher the odds of fruitful treatment.. To upgrade malignancy location the radiologists, utilizes CT check pictures for reviewing the insides of the body.Image handling methods give a decent quality apparatus to enhancing the manual examination. Henceforth, a lung malignancy recognition framework utilizing picture handling is utilized to arrange the present of lung disease in a CT-pictures. A programmed growth discovery framework is proposed to recognize malignant tumor from the CT check pictures. The tumor discovery conspire comprises of four phases. They are preprocessing, division, include extraction and characterization. These four levels are utilized as a part of picture handling to upgrade the tumor recognizable proof exactness. The ultimate result of this paper is to discover malignancy identification.

Improved ROEWA SAR Image Edge Detector Based on Curvilinear Structures Extraction

By introducing curvilinear structures extraction (CSE) instead of watershed algorithm (WA) or nonmaximum suppression (NMS) to edge strength map (ESM), an improved ratio of exponentially weighted averages (ROEWA) edge detector with better capacity for weak edges detection is proposed to extract smooth edges and edge direction of synthetic aperture radar (SAR) images. Using the ROEWA, the ESM is calculated. Then the CSE algorithm is employed to extracted edges, by acquiring eigenvectors and eigenvalues of the Hessian matrix, the improved ESM (IESM) is obtained, which ensures the good weak edge detection capacity and smoothness of edges. Experimental results on simulated and real SAR images show that the improved ROEWA based on CSE attains better performance than the one using WA or NMS.