Region Growing Segmentation Method Research Articles

Experimental study on instability mechanism and critical intensity of rainfall of high-steep rock slopes under unsaturated conditions

Two critical factors, namely intense precipitation and intricate excavation, can trigger rock mass disasters in mining operations. In this study, an indoor rainfall system was developed to precisely regulate the flow and intensity of precipitation. A large-scale model experiment was conducted on a self-designed physical simulation experiment platform to investigate the failure and instability of high-steep rock slopes under unsaturated conditions. The real-time reproduction of the progressive failure process in high-steep rock slopes enabled the determination of the critical rainfall intensity and revealed the mechanism underlying slope instability. Experiment results indicated that rainfall may be the primary factor contributing to rock mass instability, while continuous pillar mining exacerbates the extent of rock mass failure. The critical failure stage of high-steep rock slopes occurs at a rainfall intensity of 40 mm/h, whereas a rainfall exceeding 50 mm can induce critical instability and precipitation reaching up to 60 mm will result in slope failure. The improved region growing segmentation method (IRGSM) was subsequently employed for image recognition of rock mass deformation in underground mines. Herein an error comparison with the simple linear iterative cluster (SLIC) superpixel method and the original region growing segmentation method (ORGSM) showed that the average identification error in the X and Y directions by the method was reduced significantly (1.82% and 1.80% in IRGSM; 4.70% and 6.26% in SLIC; 9.45% and 12.40% in ORGSM). Ultimately, the relationship between rainfall intensity and failure probability was analyzed using the Monte Carlo method. Moreover, the stability assessment criteria of rock slope under unsaturated condition were quantitatively and accurately evaluated.

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model.

Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.

A new point cloud simplification method with feature and integrity preservation by partition strategy

The advancement of 3D scanning technology has gradually brought to light the issue of complex and time-consuming processing of high-density point cloud data. To address this need, this article proposes a method for point cloud simplification based on the concept of partitioning, which divides the point cloud's points into edge points, feature points, and non-feature points. On the basis of the normal angular difference, the edge points are extracted from the entire point cloud. The point cloud is then segmented by curvature into feature and non-feature regions using a region growing segmentation method. The feature points are determined by calculating the information entropy of the points in the feature region, whereas the non-feature points are extracted by voxel down-sampling the non-feature points. The experiment demonstrates that the proposed method effectively preserves the features and integrity of the point cloud while requiring less computational effort.

Artificial intelligent techniques applied for detection COVID-19 based on chest medical imaging

One of the ways to detect coronavirus disease of 2019 (COVID-19) is X-rays, computerized tomography (CT). This paper aims to detect COVID-19 from CT images without any user intervention. The proposed algorithm consists of 5 stages. These stages include; the first stage aims to collect data from hospitals and internet websites, the second stage is pre-processing stage to remove noise and convert it from red green blue (RGB) to grayscale and then improve image quality, the third is the segmentation stage which included threshold and region-growing segmentation methods. The fourth stage is used to extract important characteristics, and the last stage is classification CT images using feed forward back propagation network (FFBPN) and support vector machines (SVM) and compare the results between them and see if the person is infected or healthy. This study was implemented in MATLAB software. The results showed that the noise cancellation technology using anisotropic filtering gave the best results. Region-growing method was reliable to separate COVID-19 infected from healthy regions. The FFBPN has given the best results for detecting and classifying COVID-19. The results of the proposed methodology are rapid and accurate in detecting COVID-19. The output from classifier is displayed on the Rasbperry Pi that included weather if patient is infected or not and the severity of COVID-19 infection.

The Identification of Stomach Cancer with Semi-Automatic Region Growing Segmentation Method

This study was conducted so as to identify the cancerous area in the stomach in a semi automatic way using image processing techniques. The aim was to identify the area suspected of being cancerous by studying the point which the doctor suspected of being cancerous in the patients. Although we cannot say that the whole area which was diagnosed as cancerous, it would be helpful for doctors to identify the area and the site of the biopsy taken.

An image recognition method for the deformation area of open-pit rock slopes under variable rainfall

Due to human mining action, relatively fragile open-pit mine rock slopes are prone to instability induced by heavy rain. Accurately identifying the information and area of deformation features on rock slopes is a key step for landslide disaster warning and prevention. This study simulates the deformation process of a large and steep rock slope in Dexing City, Jiangxi Province, China, under a variable rainfall event. The test results show that 1) the increased deformation region has positive relationships with increasing pore water pressure and water content values for the open-pit mine rock slope affected by heavy rainfall. 2) The infiltration of variable rainfall on the rock slope softens the weak interlayers and leads to failure of the slope toe first. Due to the action of gravity, the middle and upper parts slide and fail in sequence. In addition, an improved region growing segmentation method (IRGSM) is proposed based on the combination of multipixel seed points and point cloud coordinates for the image recognition of the deformation area of open-pit mine rock slopes. An error comparison with the original region growing segmentation method (ORGSM) shows that the average identification error in the X and Y directions by the method is reduced significantly (6.56% and 5.32% in IRGSM; 13.37% and 11.29% in ORGSM). This method may be applied to identify rock slope deformation in complex scenes with high precision.

Automatic Measurement of Morphological Traits of Typical Leaf Samples.

It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.

Toward stronger energy compaction for high capacity dct-based steganography: a region-growing approach

It has been proven that the strong energy compaction property of the Discrete Cosine Transform (DCT) has a relation with the inter-pixel correlation level in the spatial domain of the cover image. Recent studies have proven that the higher the correlation between pixels in the spatial domain, the stronger the energy compaction property of the DCT. Therefore, several state-of-the-art image hiding schemes aim to increase the homogeneity by segmenting the cover image into homogeneous segments to exploit the strong compaction property of the DCT. Early attempts had adopted the idea of segmenting the cover image with fixed-sized blocks to increase the homogeneity. In other attempts, the homogeneity was increased by utilizing the quad-tree segmentation technique, which showed improved results in hiding capacity and stego quality compared to the fixed-block based hiding scheme. This paper proposes the Adaptive Region-Growing (ARG) image hiding scheme, which aims to maximize the inter-pixel correlation level using the region-growing segmentation method. This segmentation technique has the ability to precisely segment the cover image into free-shaped homogeneous regions. Since objects in an image are usually free-shaped regions, it is expected that segmenting into free-shaped regions will maximize the homogeneity level within a region more than block-based segmentation techniques. Therefore, experimental results have shown superior performance of the proposed ARG scheme over competitive steganography techniques in both the hiding capacity and the quality of the stego image that reached up to 40.36 dB at 22.34 bpp.

Increasing the Performance of the Iterative Computed Tomography Image Reconstruction Algorithms

Computed tomography (CT) imaging is an important diagnostic tool. CT imaging facilitates the internal rendering of a scanned object by measuring the attenuation of beams of X-ray radiation. CT employs a mathematical technique of image reconstruction; those techniques are classified as; analytical and iterative. The iterative reconstruction (IR) methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time, those methods are excluded from routine use in clinical applications. In this paper the reconstruction time of an IR algorithm is minimized through the employment of an adaptive region growing segmentation method that focuses the image reconstruction process on a specified region, thus ignoring unwanted pixels that increase the computation time. This method is tested on the iterative algebraic reconstruction technique (ART) algorithm. Some phantom images are used in this paper to demonstrate the effects of the segmentation process. The simulation results are executed using MATLAB (version R2018b) programming language, and a computer system with the following specifications: CPU core i7 (2.40 GHz) for processing. Simulation results indicate that this method will reduce the reconstruction time of the iterative algorithms, and will enhance the quality of the reconstructed image.

A new Construction Method of Vehicle Digital Camouflage Spraying Model

Digital camouflage plays an important role in improving the combat performance of vehicles. The reconstruction of the spraying model of the automobile surface can improve the automation level of the digital camouflage painting operation. The surface digital camouflage spraying model construction method is a processing flow that incorporates a variety of technical methods, including surface point cloud data collection technology, point cloud synthesis and segmentation method, surface model reconstruction method and spray pattern fusion technology. Four linear laser sensors are used to scan the vehicle surface to obtain the local point cloud data of the vehicle. The vehicle CAD model and the painting pattern figures are analysed. And the region growing segmentation method is used to subdivide the scanned point cloud data. For the point cloud after partitioning, the point set of the digital camouflage pattern is directly searched by using the constructed discrete metric. Then each point cloud is merged with the 2D camouflage pattern to obtain the digital camouflage spraying model of the vehicle surface.

A hybrid region growing tumour segmentation method for low contrast and high noise Nuclear Medicine (NM) images by combining a novel non-linear diffusion filter and global gradient measure (HNDF-GGM-RG)

Poor spatial resolution and low signal-to-noise ratio (SNR) along with the finite image sampling constraint make lesion segmentation on Nuclear Medicine (NM) images (e.g., PET–Positron Emission Tomography) a challenging task. Since the size, signal-to-background ratio (SBR) and SNR of lesion vary within and between patients, performance of the conventional segmentation methods are not consistent against statistical fluctuations. To overcome these limitations, a hybrid region growing segmentation method is proposed combining non-linear diffusion filter and global gradient measure (HNDF-GGM-RG). The performance of the algorithm is validated on PET images and compared with the 40%-fixed threshold and a state-of-the-art active contour (AC) methods. Segmented volume, dice similarity coefficient (DSC) and percentage classification error (% CE) were used as the quantitative figures of merit (FOM) using the torso NEMA phantom that contains six different sizes of spheres. A 2:1 SBR was created between the spheres and background and the phantom was scanned with a Siemens TrueV PET-CT scanner. 40T method is SNR dependent and overestimates the volumes (≈4.5 times). AC volumes match with the true volumes only for the largest three spheres. On the other hand, the proposed HNDF-GGM-RG volumes match closely with the true volumes irrespective of the size and SNR. Average DSC of 0.32 and 0.66 and % CE of 700% and 160% were achieved by the 40T and AC methods respectively. Conversely, average DSC and %CE are 0.70 and 60% for HNDF-GGM-RG and less dependent on SNR. Since two-sample t-test indicates that the performance of AC and HNDF-GGM-RG are statistically significant for the smallest three spheres and similar for the rest, HNDF-GGM-RG can be applied where the size, SBR and SNR are subject to change either due to alterations in the radiotracer uptake because of treatment or uptake variability of different radiotracers because of differences in their molecular pathways.

A smoke segmentation algorithm based on improved intelligent seeded region growing

SummaryImage segmentation method based on region growing has the advantages of simple segmentation method and complete segmentation target. Because the color discrimination and gray gradient of smoke are not obvious, the traditional region growing segmentation method is difficult to separate it from the image, resulting in an unsatisfactory segmentation effect. To solve this problem, this paper partially improved the region growing method and proposed a new smoke segmentation algorithm based on the improved intelligent seeded region growing (IISRG) method. First, smoke images obtained from experimental videos were converted from the RGB color space to the HSV color space, and image binarization was achieved using background subtraction with an adaptive threshold in the V channel. Then, a pixel in the binary image was selected intelligently as the seed point, which was used for the regional growth. The final smoke segmentation images were obtained by the morphological processing of region growing images. Experimental smoke segmentation results show that the proposed algorithm has a higher overlap rate and a lower overflow rate, and performs a better smoke segmentation effect compared with the other two approaches. In addition, this algorithm can also effectively solve the problems of under‐segmentation and over‐segmentation of smoke images.

Perbandingan Metode Segmentasi K-Means Clustering dan Segmentasi Region Growing untuk Pengukuran Luas Wilayah Hutan Mangrove

This study aims to examine the k-means clustering and region growing segmentation methods to identify and measure the area of mangrove forests in the Southeast Sulawesi province. The image of the area of this study used Landsat 8 satellite imagery. The area of mangrove forest was carried out by calculating the number of pixels identified as mangrove forests with an area density of 900 m2/pixel. The accuracy of the two segmentation methods in calculating the area was compared based on the same area calculated by LAPAN. The overall accuracy of k-means clustering segmentation method has better accuracy, which is 59.26%, than region growing with 33.33% of accuracy. Both image segmentation methods, k-means clustering and region growing, can be used to calculate the area of mangrove forests in the Southeast Sulawesi region using Landsat 8 satellite imagery.

Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data.

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

Segmentation of building roofs from airborne LiDAR point clouds using robust voxel-based region growing

ABSTRACTThe research herein presents a new approach for extracting building roofs using a robust voxel-based region growing segmentation method. The proposed approach exploits the fact that the roof of the building consists of planar surfaces and has distinctive geometric features than other kinds of objects. Based on this assumption, we present a method using voxel structure and region growing strategy with robust principal component analysis (RPCA). The voxels is clustered by a region growing process, utilizing the smoothness, continuity, and convexity as geometric cues. RPCA is introduced to estimate the attribute of voxels. Roofs are recognized from the segments by using the object-based spectral clustering. Our approach has been validated by different airborne laser scanning (ALS) point clouds. Qualitative and quantitative results reveal that our method outperforms some representative algorithms in segmentation using our testing datasets under a complex situation, with overall quality measure better than 0.7 and 0.6.

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

Sample K-Means Clustering Method for Determining the Stage of Breast Cancer Malignancy Based on Cancer Size on Mammogram Image Basis

Breast cancer is a disease that arises due to the growth of breast tissue cells that are not normal. The detection of breast cancer malignancy level / stage relies heavily on the results of the analysis of the doctor. To assist the analysis, this research aims to develop a software that can determine the stage of breast cancer based on the size of the cancerous tissue. Steps of the research consist of mammogram image acquisition, determining the ROI (Region of Interest), using Region growing segmentation method, measuring the area of suspected cancer, and determine the stage classification of the area on the mammogram image by using Sample K-Means Clustering method. Based on 33 malignant (abnormal) mammogram sample images taken from the mini mammography database of MIAS, the proposed method can detect stage of breast cancer is in malignant group.

Mass Detection in Lung CT Images using Region Growing Segmentation and Decision Making based on Fuzzy Systems

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. In o rder to help specialists in the search and recognition of the lung nodules in tomography images, a good number of research centers have been developed in computer-aided detection (CAD) systems for automating the procedures. This work aims at detecting lung nodules automatically through computerized tomography images. Accordingly, this article aim at presenting a method to imp rove the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images and, then, cancer recognition by FIS (Fu zzy Inference System). The proposed method consists of three steps. The first step was pre-processing for enhancing contrast, removing noise, and pictures less corrupted by Linear- Filtering. In second step, the region growing segmentation method was used to segment the CT images. In third step, we have developed an expert system for decision making which d ifferentiates between normal, benign, malignant or advanced abnormality findings. The FIS can be of great help in diagnosing any abnormality in the medical images. This step was done by ext racting the features such as area and color (gray values) and given to the FIS as input. This system utilizes fu zzy membership functions which can be stated in the form of if-then ru les for finding the type of the abnormality. Finally, the analysis step will be discussed and the accuracy of the method will be determined. Our experiments show that the average sensitivity of the proposed method is more than 95%.

Mass Detection in Lung CT Images Using Region Growing Segmentation and Decision Making Based on Fuzzy Inference System and Artificial Neural Network

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. This work aims at detecting lung nodules automatically through computerized tomography (CT) image. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images. Afterwards, cancer recognition are presenting by Fuzzy Inference System (FIS) for differentiating between malignant, benign and advanced lung nodules. In the following, this paper is testing the diagnostic performances of FIS system by using artificial neural networks (ANNs). Our experiments show that the average sensitivity of the proposed method is 95%.