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Identifying and separating objects within an image is a significant challenge due to high object and background variability. This can be obtained through feature extraction approach. There are different ways of extracting the image features. It is based on texture, shape and colour. The present paper aims to study and analyse the various approaches for feature extraction and object recognition. This study proposed a hybrid approach, which is a combination of enhanced Fractal Texture Analysis with Layout Descriptor to overcome the obstacles in image segmentation. It is used to lessen the boundary complexity of the segmented image. First, the image is preprocessed to discard the noise and to retain the adequate details of the image in a perfect way through Adaptive Switching Median Filter. Secondly, it improves the power of the edges detected through a noise-protected edge detector. Finally, it is applied with morphological gradient technique that is a twin function of both shape and texture gradient removal for extorting the qualities of the image. In this way, the proposed methodology directly performs on the colour image which supports to enhance prediction accuracy of the object in terms of colour characteristics that offers better results than the grayscale conversion approach. Moreover, the shape feature is extracted from the preprocessed image depending on the details like compactness, rectangularity, eccentricity and moment invariants.

Road edge detection from remote sensing images, as an important ground object type, plays an important role in people's life and travel and urban planning and development, and extracting road information from remote sensing images has practical scientific value and practical significance. However, with the development of remote sensing technology, while the resolution of remote sensing images is improved, the information describing ground objects becomes more and more abundant, and the difficulty of identifying and extracting road information is also increased. In the process of acquiring remote sensing images, the actual system is subjected to various kinds of noise interference. Different environmental interference and system defects will introduce noises with completely different distribution and statistical characteristics to remote sensing images. Aiming at the problem that the detection effect of traditional algorithms becomes worse due to the influence of noise on remote sensing images, a wavelet transform denoising method and morphological gradient operator are proposed. By selecting appropriate structural elements of remote sensing images, noise pixels cannot participate in morphological calculation, and the noise intensity changes with the size of quantum superposition state structural elements. Therefore, a morphological gradient operator is established and applied to edge detection of remote sensing images. Finally, the experimental results show that the method proposed in this article is better than other directions in terms of effect through road edge detection and matching. This method can effectively reduce noise. Compared with other algorithms, the method proposed in this article has certain advantages.

For the unmanned surface vehicle (USV) autonomous navigation and obstacle avoidance in the inland waters and coastal areas, it's essential to understand the water scene and divide the navigable area by taking the sea-sky-line and shoreline as the reference. Owing to the noise interference such as water surface reflection, it is not easy to detect the sea-sky-line and shoreline. In order to segment the water scene more accurately, the gradient image is firstly generated by Sobel operator, and then the contour of sea-sky-line and shoreline is enhanced by superposition with the image eliminated by sea surface reflection. Considering the local features in each image partition, superpixels are generated by multi-scale morphological gradient reconstruction (MMGR) and watershed algorithm. Finally, the superpixels is aggregated by fuzzy c-means (FCM), to get water scene segmentation. Experimental results show that the algorithm proposed (SEFCM) is superior to other similar algorithms in the accuracy of water scene segmentation, and more robust to illumination interference.

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are time-consuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than state-of-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation. China Postdoctoral Science Foundation; National Natural Science Foundation of China; National Science Foundation of Shanghai

Image segmentation techniques play a vital role in partitioning the image into segments. These techniques are used for detecting the objects in the images. The real challenge in the acoustic image segmentation lies in differentiating the sea floor, sediments and the objects. Due to the typical characteristics and low resolution of the acoustic images, segmentation techniques such as region based, morphological based and edge based methods are not suitable. The sediments will also be traced along with the objects that makes the detection process a very difficult one. In this research paper, edge based segmentation method that uses the morphological operations for identifying the edges followed by a object tracing algorithm is proposed. The images used in this work are captured in real time using Edgetech 4125 Side scan sonar device. The acoustic images are first pre-processed using Wiener and Median filters and a morphological gradient is obtained by subtracting the morphologically dilated and eroded image. Next the edge map of the acoustic images are obtained using binarization method by which the object’s boundary is made visible. Finally a Moore’s object tracing algorithm is used for detecting the objects in the images.

Rock particle image segmentation is typically the first and most difficult task [59]. All subsequent interpretation tasks, including particle size, shape and texture analysis, rely heavily on the quality of the segmentation results. Since rock particle images vary from one to another, it is difficult or impossible to design and develop one segmentation algorithm for all kinds of rock particle images. The presented segmentation algorithms were developed for just several types of rock particle images with a certain characteristics with respect to segmentation. In general, both two steps of rock particle image segmentation, i.e. segmentation based on gray level and segmentation based on shape and size of rock particle particles, are needed in most cases of rock particle application. The thresholding algorithms are for images where particles or particle clusters differ everywhere in intensity from the background. The thresholding algorithms are not sensitive to texture on particles, and have normally a low cost for processing. When particles are densely packed, and particles are surrounded by particles and some void spaces (background), the algorithm based on split-and-merge can be applied for the image. The algorithm based on split-and-merge has the advantage of producing higher level primitives, but the region so extracted may not correspond to actual particles, and the boundary of the extracted region is rough. The algorithm based on edge detection is suitable for the images without too much texture on the surface of particles. It has the disadvantage of producing low-level primitives (segments) even after considerable processing. As one example, the two algorithms are compared in Fig. 23. In Fig. 23(a), the original image is a

A large number of improved variants of the Fuzzy c-means were used to segment the gray scale and color image. Nevertheless, some of them take time to yield the best and optimal results and for two reasons cannot produce adequate segmentation results for color images. This work proposes a simple FCM clustering algorithm based on the Super pixel Algorithm (SFFCM), which is considerably faster and more robust for image segmentation applications which are based purely on the color parameter. In this research work, to attain an accurate contour super pixel image for improved local spatial neighborhoods, an efficient algorithm referred as multi-scale morphological gradient reconstruction (MMGR) operation is originally described. The significance of the proposed method lies in the relationships between the pixels and how they are utilized in the flow of the application. Compared to the conventional adjacent fixed-size and shape frame, the super pixel image provides improved adaptive and exact amount of irregular local spatial communities which help to enhance color image segmentation. Coming to the next step, the original color image is essentially based on the obtained super pixel picture, and the number of pixels in each super pixel region easily decides its histogram. Ultimately, FCM is implemented to obtain final segmentation results that increase the histogram parameter on the super pixel picture. The performance of the proposed method is computed using Matlab computing language and also a statistical measure is carried out based on different parameters and their respective graphical representations.

The paper describes an approach to the image borders detection that is based on the use of the weight model and the operation of the morphological gradient calculating. The proposed method relates to multiscale image segmentation methods that use the Haar orthogonal wavelet transform. The border detection procedure includes smoothing the original image, building the weight model of the image (weight image), smoothing the weight image, setting the threshold weight value, performing threshold transformation of the weight image to form a binary image, and performing gradient morphological processing of the binary image. The described method allows segmenting the original image to extract information about the borders. This information is used to calculate attributes and their analysis in computer vision systems.

Background and Objective Brain tumor segmentation is a challenging issue due to noise, artifact, and intensity non-uniformity in magnetic resonance images (MRI). Manual MRI segmentation is a very tedious, time-consuming, and user-dependent task. This paper aims to presents a novel level set method to address aforementioned challenges for reliable and automatic brain tumor segmentation. Methods In the proposed method, a new functional, based on level set method, is presented for medical image segmentation. Firstly, we define a superpixel fuzzy clustering objective function. To create superpixel regions, multiscale morphological gradient reconstruction (MMGR) operation is used. Secondly, a novel fuzzy energy functional is defined based on superpixel segmentation and histogram computation. Then, level set equations are obtained by using gradient descent method. Finally, we solve the level set equations by using lattice Boltzmann method (LBM). To evaluate the performance of the proposed method, both synthetic image dataset and real Glioma brain tumor images from BraTS 2017 dataset are used. Results Experiments indicate that our proposed method is robust to noise, initialization, and intensity non-uniformity. Moreover, it is faster and more accurate than other state-of-the-art segmentation methods with the averages of running time is 3.25 seconds, Dice and Jaccard coefficients for automatic tumor segmentation against ground truth are 0.93 and 0.87, respectively. The mean value of Hausdorff distance, Mean absolute Distance (MAD), accuracy, sensitivity, and specificity are 2.70, 0.005, 0.9940, 0.9183, and 0.9972, respectively. Conclusions Our proposed method shows satisfactory results for Glioma brain tumor segmentation due to superpixel fuzzy clustering accurate segmentation results. Moreover, our method is fast and robust to noise, initialization, and intensity non-uniformity. Since most of the medical images suffer from these problems, the proposed method can more effective for complicated medical image segmentation.

In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image is an important step. The majority of methods are not able to give an integrated structure for finding fast and effective tumor segmentation. Hence segmentation of tumor is most difficult task in diagnosing. In this paper, CT abdominal image is segmented using Superpixel-based fast Fuzzy C Means clustering algorithm to decrease the time needed for computation and eradicate the manual interface. In this algorithm, a superpixel image with perfect contour can be obtain using a Multiscale morphological gradient reconstruction operation. Superpixel is pre-segmentation algorithm and is employed to obtain segmentation accuracy. FCM with modified object is used to obtain the color segmentation. This method is examined on 20 CT images gathered from liveratlas database, results shows that this approach is fast and accurate compared to most of segmentation algorithms. Statistical parameters which include accuracy, precision, sensitivity, specificity, dice, rfn and rfp are calculated for segmented image. The results shows that this algorithm gives high accuracy of 99.58% and improved rfn value of 8.34% compared with methods discussed in the literature.

Rapid growth in the field of quantitative digital image analysis is paving the way for scientific researchers to make precise measurements about objects in an image. To compute quantities from an image such as the density of compressed materials or the velocity of a shockwave, object boundaries must first be determined. Images containing regions that each have a spatial trend in intensity are of particular interest here. For edge detection, we present a supervised, statistical image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms, specifically for images where the regions are known but precise boundary locations are unknown. The segmentation of a pixel is determined by comparing its intensity to distributions from nearby pixel intensities, and a gradient of the segmented image indicates edge locations. Because of the statistical nature of the algorithm, we use maximum likelihood estimation to quantify uncertainty about ea...

Research on changes in the fluid edge of a wave flume is important for experimental hydrodynamics. However, disturbances often occur because of the presence of sensors. To solve this problem, a new grey-scale image processing method for fluid edge analysis is presented here. By fusing methods combining image gradients and image segmentation with shifting-window technology and with concepts derived from experimental fluid mechanics, the proposed method can overcome many of the inherent challenges of fluid-edge measurement. First, the geodesic distance is modified to obtain a class curve. Second, an edge position is determined by the inflection point of the class curve related to the gradient peak distribution. Next, the position of the interrogation window is relocated with reference to neighbors or to previous results, and the current edge position can be calculated according to the predicted value. During the computation, the interrogation window can change its position adaptively with fluid motion, ensuring that the amount of data to be analyzed always remains stable. A model combining the class curve and gradient curve can improve the validity of edge identification. Finally, the performance of the proposed method has been evaluated using images in a glass flume. The results show that the proposed method for studying the fluid edge is effective and robust.

At present, the image restoration algorithm based on sample block is not ideal for image processing with abundant texture information, and it is easy to produce false matching texture blocks in the process of repairing. An improved image inpainting algorithm based on image segmentation is proposed in this paper to address this problem. Firstly, the watershed image segmentation algorithm is used for image segmentation, which is combined with curvature feature of isophotes to repair the image. Curvature features reflecting the details of texture character are introduced in the data items to calculate the priority. Secondly, the matching of the pixel block must be searched in the image segmentation region, which can be a better solution to avoid the phenomenon of excessive extension of the texture block in the process of image restoration. Compared with the original Criminisi algorithm and other improved algorithms, the experimental result shows that the method we proposed have a larger PSNR value of image, which means to have better repair effects on the image including abundant texture information.

Ultrasound imaging is one of the imaging techniques in medicine in which the images resulted from ultrasonic wave propagation are used for diagnostic applications. Digital processing of these images for reasons is a difficult process due to causes such as noise, same distribution of intensity values, and unknown border of organs from image tissue. In this study, image segmentation and improving image and improving quality of the image segmentation was done during three stages: preprocessing, processing and post-processing. Multi-agent dimensional structure is selected which has the best result in the image segmentation. In the processing reinforcement learning factor, threshold operator values and opening operator dimensions obtained for image segmentation. In post-processing process, for improving the quality of image segmentation, the most appropriate dimensions are determined for morphology operators. The results of implementing the presented method on the sample of the prostate is displayed.

Superpixel-based fast fuzzy C-means clustering (SFFCM) is an efficient method for color image segmentation. However, it is sensitive to noise and blur. Its superpixel method called multiscale morphological gradient reconstruction (MMGR) is time consuming. In this paper, we propose an improved SFFCM method (ISFFCM) which replaces the MMGR in SFFCM with fuzzy simple linear iterative clustering (Fuzzy SLIC). Fuzzy SLIC is faster and more robust than MMGR for most types of noise, including salt and pepper noise, Gaussian noise and multiplicative noise. It is also more robust to image blur. In the validation experiments, we tested ISFFCM and SFFCM on the Berkeley benchmark. The experiment results show that our method outperforms SFFCM under noise and blurring environments.

Image segmentation is a necessary but difficult task in many image processing applications. Unlike conventional auto-segmentation, semi-supervised image segmentation involves a moderate amount of user interaction. In this paper, a novel interactive image segmentation method based on diffusion maps is proposed, which can better account for the distribution of pixels in feature space. Our method replaces the pixel features of Euclidean distance with diffusion maps and performs feature optimization and dimensionality reduction in order to achieve a final feature vector, with the assistance of user interaction. On this basis, L0 gradient minimization is applied to achieve steady segmentation results. Finally, the K-means clustering algorithm is applied to perfect the segmentation. Experiments show that our method can obtain better segmentation results than several state-of-the-art interactive image segmentation methods.

age Segmentation is an essential procedure of image processing as well as knowing it. Mainly it's known due to the method of splitting the image into various elements of homogeneity. The goal of image segmentation is to make simpler presentation of an image into incredible which is meaningful as well as easy to understand. It is mainly utilized to know the location of objects, boundaries, lines etc in the digital images. Clustering technique is a method which shows the data set or pixels are replaced by cluster, pixels might be together because of the same color, texture etc. This paper represents the effect of mixed regions on image segmentation and color images contain maximum information for efficient image segmentation. KeywordsProcessing,Image Segmentation Techniques

This paper proposes a novel automatic reference color selection (ARCS) scheme for the adaptive mathematical morphology (MM) method, and is specifically designed for color image segmentation applications. Because of the main advantages of being intuitive and simple, in the past decade, it has contributed to the growing popularity of binary and gray-scale MM processing. However, the MM process typically neglects the details of reference color determination. Applying other ordering methods, which select only black as the reference color for sorting pixels, result in the problem in which the scope of the distance measurement is not optimal. The proposed ARCS scheme is used for determining the ideal reference color for MM and for color image segmentation application. In addition, we use both 1D histogram-based modeling scheme binning from 3D color spaces, such as red-green-blue and hue-saturation-intensity, and 2D color models, such as (H, S), (Cb, Cr), and (I, By). According to the results of the quartile analysis, the threshold determination reacts with less sensitivity to the context variations of the images tested. The experiments focused on color-based image segmentation using the proposed ARCS scheme for color MM processing through a bottom-up scenario. To evaluate the system, four quantitative indices were utilized for an ARCS comparison using advanced segmentation methods in the experiments. The cross validation with different system parameters and a comparison of the morphological gradient operation with different color models are also presented.

A novel model of image segmentation based on watershed method is proposed in this work. To prevent the over segmentation of traditional watershed, our proposed algorithm has five stages. Firstly, the morphological reconstruction is applied to smooth the flat area and preserve the edge of the image. Secondly, multiscale morphological gradient is used to avoid the thickening and merging of the edges. Thirdly, for contrast enhancement the top/bottom hat transformation is used. Fourthly, the morphological gradient of an image is modified by imposing regional minima at the location of both the internal and the external markers. Finally, a weighted function is used to combine the top/bottom hat transformation algorithm and the markers algorithm to get the algorithm. The experimental results show the superiority of the new algorithm in terms of suppression over segmentation.

Watershed transformation is an effective segmentation algorithm that originates from the mathematical morphology field. This algorithm is widely used in medical image segmentation because it produces complete division even under poor contrast. However, over-segmentation is its most significant limitation. Therefore, this article proposes a combination of watershed transformation and the expectation-maximization EM algorithm to segment MR brain images efficiently. The EM algorithm is used to form clusters. Then, the brightest cluster is considered and converted into a binary image. A Sobel operator applied on the binary image generates the initial gradient image. Morphological reconstruction is applied to find the foreground and background markers. The final gradient image is obtained using the minima imposition technique on the initial gradient magnitude along with markers. In addition, watershed segmentation applied on the final gradient magnitude generates effective gray matter and cerebrospinal fluid segmentation. The results are compared with simple marker controlled watershed segmentation, watershed segmentation combined with Otsu multilevel thresholding, and local binary fitting energy model for validation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 225-232, 2016

A new segmentation approach for high-resolution remotely sensed imagery that combines the global edge and region information is developed from a new scheme to monitor the best conditions for each growing object to obtain the corresponding meaningful image object during multiscale analysis. The approach, which is an extension of the image object detection approach, includes new algorithms for determination of region-growing criteria, edge-guided image object detection, and assessment of edges. The method consists of two stages: In the first stage, edges are acquired from edge detection with embedded confidence and stored in an R-tree, and initial objects are segmented by eCognition and organized in the region adjacency graph; in the second stage, meaningful image objects are obtained by incorporating multiscale segmentation and analyzing the edge completeness curve. The evaluation results of edge completeness are obtained within the process of multiscale segmentation, and the assessment for the segmentation results shows its merit in coastal remote sensing. Images containing plenty of weak edges or distributing scene objects with various sizes and shapes can fully embody the strength of this method.

An effective multi-object C-V segmentation model is proposed.A rapid initial contour search is conducted.To achieve the multi-object segmentation the image is converted to a single level set.The evolution of the curve is controlled adaptively via the contour line gradient.The model takes the local characteristics of each target into account through the broad contours to avoid missing the target. The Chan-Vese (C-V) model is an ineffective method for processing images in which the intensity is inhomogeneous. This is especially true for multi-object segmentation, in which the target may be missed or excessively segmented. In addition, for images with rich texture information, the processing speed of the C-V is slow. To overcome these problems, this paper proposes an effective multi-object C-V segmentation model based on region division and gradient guide. First, a rapid initial contour search is conducted using Otsu's method. This contour line becomes the initial contour for our multi-object segmentation C-V model based on a gradient guide. To achieve the multi-object segmentation the image is then converted to a single level set whose evolution is controlled using an adaptive gradient. The feasibility of the proposed model is analyzed theoretically, and a number of simulation experiments are conducted to validate its effectiveness.

Image segmentation is one of the most important and challenging problems in image processing. The main purpose of image segmentation is to partition an image into a set of disjoint regions with uniform attributes. In this study, we propose an improved method for edge detection and image segmentation using fuzzy cellular automata. In the first stage, we introduce a new edge detection method based on fuzzy cellular automata, called the texture histogram, and empirically demonstrate the efficiency of the proposed method and its robustness in denoising images. In the second stage, we propose an edge detection algorithm by considering the mean values of the edges matrix. In this algorithm, we use four fuzzy rules instead of 32 fuzzy rules reported earlier in the literature. In the third and final stage, we use the local edge in the edge detection stage to more accurately accomplish image segmentation. We demonstrate that the proposed method produces better output images in comparison with the separate segmentation and edge detection methods studied in the literature. In addition, we show that the method proposed in this study is more flexible and efficient when noise is added to an image.

Edge-based active contour models are effective in segmenting images with intensity inhomogeneity but often fail when applied to images containing poorly defined boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize only gradient information, which fails to stop contour evolution at such boundaries because of the small gradient magnitudes. To address this problem, we propose a framework to construct a group of ESFs for edge-based active contour models to segment objects with poorly defined boundaries. In our framework, which incorporates gradient information as well as probability scores from a standard classifier, the ESF can be constructed from any classification algorithm and applied to any edge-based model using a level set method. Experiments on medical images using the distance regularized level set for edge-based active contour models as well as the k-nearest neighbours and the support vector machine confirm the effectiveness of the proposed approach.

In order to improve the monitoring of water surface environment, the image processing of large-scale water pollution is proposed. First, UAV was used to take aerial photos of the area to be cleaned on the water, and the obtained image was preprocessed with saturation enhancement, etc. Then the image was segmented into image segmentation or edge detection to distinguish the foreground from the background. This experiment used three widely used segmentation methods to process the selected aerial image. The three methods are: threshold segmentation, region-based segmentation and morphological gradient detection.

The segmentation of lettuce is the key monitoring link in plant factory. First through the image information collector to collect a lettuce image, we analyzed the image of the morphological gradient image. Then the image of the lettuce was morphological and morphological, and the foreground image and background image of the image were marked. Finally lettuce and the background of the image based on the watershed algorithm for precise segmentation. The experimental results shown that the morphological recombination and watershed algorithm can accurately divide the images of the lettuce. This paper applies morphological recombination and watershed algorithm to plant factory, which provided reference basis for image processing in agricultural production and application.

Image segmentation is a vital step in many imaging applications, such as medical images and computer vision. Image segmentation is considered as a challenging problem, so we need to develop an efficient, fast technique for medical image segmentation. In this paper, we propose a new system for a multi-resolution MRI brain image segmentation, which is based on a morphological pyramid with fuzzy C-mean (FCM) clustering. In the first stage, we use a wavelet multi-resolution to maintain spatial context between pixels. Secondly, we use the morphological pyramid to fuse the resulting multi-resolution images with the original image to increase sharpness and decrease noise in the processed image. Finally, we use FCM technique to segment the processed images. We compared our proposed system with some state of the art segmentation techniques on two different brain data sets. Experimental results showed that the proposed system improves the accuracy of the MRI brain image segmentation.

In this paper, we propose a new multi-scale morphological approach to curve evolution useful for object extraction through segmentation. The homogenous image structures that characterize the segmentation process are edges and terminations. Normally the conventional morphological snake (MS) technique employs morphological binary level-set operators for realizing forces. These operations handle definite components of the PDE (partial differential equation) used for modeling the dynamic system. The proposed model can segment with reasonably high level of accuracy and efficiency while ensuring smooth segmentation at object boundaries with scale space continuity. Application of discrete image force in MS is a per pixel decision based on the sign of image force PDE component. In the continuous domain however, the intensity of the image force PDE component is the primary factor for snake evolution. In our model we embed scale-space continuity into the morphological operators dictated by MS in order to realize the image force both in intensity and direction. Thus, our model confirms to the speed, agility and robustness of morphological snakes with regard to segmentation while ensuring enhanced efficiency of segmentation under noise. We have rated the performance both on qualitative and quantitative basis against benchmark results, on a set of 2D gray-scale real images both in absence and presence of noise. A comparative study has also been carried among our method, MS, geodesic active contour (GAC) and Distance Regularized Level Set Evolution (DRLSE). HighlightsMulti-scale morphological approach to curve evolution is suggested for segmentation.We embed scale-space continuity in morphological snakes.Morphological operators ensure fast curve evolution.Scale-space continuity ensures smoother segmentation near edges.Extraction improvement near noisy pixels is observed with scale-space continuity.

A new technique for the segmentation of single- and multiresolution (MR) remote sensing images is proposed. To guarantee the preservation of details at fine scales, edge-based watershed is used, with automatically generated markers that help in limiting oversegmentation. For MR images, the panchromatic and multispectral components are processed independently, extracting both the edge maps and the morphological and spectral markers that are eventually fused at the highest resolution, thus avoiding any information loss induced by pansharpening. Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.

This letter presents an edge direction adaptive watershed segmentation method for remote sensing images. First, the maximum gradient value among different directions is chosen as the single band gradient value of the pixel, and a compound gradient value is then calculated based on the gradient value in each band. Second, the marker-based watershed segmentation is implemented to produce initial over-segmentation result to avoid under-segmentation. Finally, the adjacent objects with high similarity values are merged to reduce over-segmentation, which improves segmentation accuracy. The performance of the proposed method is validated on two satellite images. Experimental results show that, compared with the multi-resolution segmentation method embedded in the eCognition software and the traditional multi-band watershed segmentation method, the proposed method can decrease over-/under-segmentation and thus produce satisfactory segmentation results.

In Image Processing, extracting the region of interest is a very challenging task. To extract information, pre-processing algorithms are important in MRI image. Edge detection is a task in which points in image are identified at which brightness changes sharply or it has discontinuities. It is an essential pre- processing step in medical image segmentation, for object recognition of the human organs. The applications of medical image segmentation are 3D reconstruction and quantitative analysis and so on. We used MRI images because MRI images give best view of tissues in any part of human body. In this paper, difficulties of edge detection in brain magnetic resonance images are considered and a new approach to edge detection is introduced. There are many traditional edge detection methods for extracting edges from images have been introduced such as gradient based operators like sobel, prewitt, robert were initially used for edge detection, but they did not give sharp edges and were highly sensitive to noise image. And in medical field accuracy is important fact. To overcome these difficulties, we proposed new method called as Active Contour method or snake model. . In the field of medical segmentation, Active contour method is one of popular research topic. This method is used for detecting brain region based on their energy function. In order to compare between them, one slice of MRI image tested with these methods. The traditional and proposed edge detection algorithms are implemented in MATLAB and results of proposed method are presented and compared with traditional approach.

This paper considers the problem of pulmonary vessels identification in thoracic 3D CT scans. In particular, the method for pulmonary vascular tree segmentation is introduced. The main idea behind the introduced method is to extract both thoracic trees together (i.e. the vascular tree and the airway tree) and then remove airway walls. Therefore, firstly segmentation of vessels and airway walls is performed using 3D region growing where the growth of the region is guided and constrained by results of random walk segmentation applied to consecutive CT slices. In particular, results of random walk segmentation of one slice are used to determine seeds for random walk segmentation of the following slice. Next step is airway tree segmentation using 3D region growing algorithm guided and constrained by the morphological gradient. Finally, morphological processing is applied in order to extend airway lumen onto airway walls and remove the overlapping regions. The main steps of the proposed approach are described in detail. Results of pulmonary vascular tree segmentation from example thoracic volumetric CT datasets provided by the introduced approach are presented and discussed. Based on a manually selected and radiologist's verified ground truth pixels and the resulting quality measures it can be concluded, that the average accuracy of the introduced approach is about 90%.

Biomedical imaging plays an imperative role in the detection process that in turn aids specialists in making an accurate diagnosis. Panoramic dental radiograph is widely used by dentists as it facilitates wider diagnosis of the j aw, teeth and soft tissue. Panoramic image requires an image processing that could generate sharper and clearer images or could improve the quality of information contained in the image. This will facilitate a better visual interpretation of it. Edge detection is the fundamental step required in many image processing applications for obtaining information from the image segmentation, feature extraction etc. However, conventional edge detection operators are not always suitable to work with different types of images due to differences in the nature of image and the variation of problems in the image. This study focus on automating edge detection of panoramic dental radiographs using Multiple Morphological Gradient Method and making use of MATLAB for implementing algorithms developed. Datasets of digital panoramic dental radiograph in JPEG format are used for edge detection of the region of interest. The results show successful implementation of methodology developed.

Liver tumor segmentation is a hot issue in current medical image processing research, fast and accurate liver tumor segmentation method for abdominal CT sequences is the basis for liver lesions diagnose. The evolving curve in traditional level set algorithm often stopped in local gradient minimal regions or false edges while dealing with low contrast or borderline blurred CT images. In order to solve this problem, a level set method based on narrowband gradient modification is proposed in this paper. First, morphological gradient transformation was performed on the image to get the gradient image. Then it set up function relationship between structural elements radius and gradient value, and modified the image gradient. Using level set method to segment the image according to gradient information. Finally using the result as the initial contour of the adjacent CT sequence, built narrowband with a special width, split the liver tumor within the narrowband, repeat the process until getting all the results of the entire slices, then 3-D reconstructed.

Graph-based segmentation is gaining popularity among the many approaches in performing image segmentation, primarily due to its ability in reflecting global image properties. The most fundamental challenge in segmentation algorithm is to precisely define the volumetric extent of some object, which may be represented by the union of multiple regions. We developed a unified framework for volumetric image segmentation and contour extraction that uses a virtual tree-hexagonal structure defined on the set of the image voxels. The most important characteristic of using a virtual tree-hexagonal network over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.

In the framework of computer-aided diagnosis of eye disease, a new contextual image feature named influence degree of average intensity is proposed for retinal vessel image segmentation. This new feature evaluates the influence degree of current detected pixel decreasing the average intensity of the local row where that pixel located. Firstly, Hessian matrix is introduced to detect candidate regions, for the reason of accelerating segmentation. Then, the influence degree of average intensity of each pixel is extracted. Next, contextual feature vector for each pixel is constructed by concatenating the 8 feature neighbors. Finally, a classifier is built to classify each pixel into vessel or non-vessel based on its contextual feature. The effectiveness of the proposed method is demonstrated through receiver operating characteristic analysis on the benchmarked databases of DRIVE and STARE. Experiment results show that our method is comparable with the state-of-the-art methods. For example, the average accuracy, sensitivity, specificity achieved on the database DRIVE and STARE are 0.9611, 0.8174, 0.9747 and 0.9547, 0.7768, 0.9751, respectively.

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image’s gradient vector flow field and the evolving contour’s normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based level set segmentation methods, particularly around weak edges.

Image segmentation is a most important operation over an image for analysis or identification purposes. Segmentation implies clear distinct delineation between object of interest and rest of the image data known as background. Segmentation algorithm is generally based on either finding the discontinuity or similarity of pixels in the local neighborhood, they are commonly termed region-based or boundary based. Most of the times, these algorithms do not produce accurate segmentation. In this paper, medical Image segmentation based on morphological operators along with threshold selection is presented. The main aim is to have better segmentation accuracy and clarity of segmented image since medical images are sensitive images.

Segmentation is a very important topic in digital image process. It is found simply in varied fields of image analysis, particularly within the medical imaging field. Axial segmentation of lungs CT scan is beneficial in designation of abnormalities and surgery planning. It will do to ascertain every section within the lungs. The results of the segmentation are accustomed discover the presence of nodules. The method which utilized in this analysis are image cropping, image binarization, Canny edge detection and morphological operation. Image cropping is done so as to separate the lungs areas, that is the region of interest. Binarization method generates a binary image that has 2 values with grey level, that is black and white (ROI), from another space of lungs CT scan image. Canny method used for the edge detection. Morphological operation is applied to smoothing the lungs edge. The segmentation methodology shows an honest result. It obtains an awfully smooth edge. Moreover, the image background can also b...

In order to segment the pig image accurately in the complex background, an image segmentation algorithm based on wavelet modulus maxima and edge growth is proposed. Firstly wavelet transformation to pig image was adopted to get local maximum value points in gray gradient, then local adaptive method was used to determine the segmentation threshold within local maximum points, and get the accurate edges. Secondly, the edge growth method based on gradient was used to link discontinuous edge points and complete the edges sealing repair. Finally, the mathematical morphology methods including connecting method and corrosion expansion operation were adopted to obtain the image area of a pig. The experimental results show that, in the complex background of piggery, the smooth and full pig image separated from background could be obtained by this algorithm. This method could improve the effectiveness of the image segmentation.

Generally, the effect of cell image that segmented via the threshold value method is not ideal generally; the found cell boundary cannot conform to the cell edge in the original picture well. In this paper, the threshold value segmentation method is improved; apply the judging criterion of gray level difference maximum interval to be the minimum, and conduct secondary treating on the image, and the image’s segmentation effect is more ideal.

This paper proposes a novel two-stage method for remote sensing image segmentation. First, initial small segments, also called subobject primitives (sub-OPs), are obtained using edge-constrained watershed segmentation and edge allocation. These segments are gradually merged into a larger segment until the edge-controlled limits are reached, thereby creating the initial OPs. In this stage, a concept of hard-boundary ratio is proposed to control the merge effectively. Second, nonconstrained merging is conducted on the OPs, which results in final segmentation. In addition, a repeatable pairwise segment-merging scheme is utilized. This scheme improves method efficiency and accuracy. Comprehensive experiments comparing this new method with the multiresolution segmentation method of eCognition were conducted. Results show that this new method has the following advantages: 1) higher segmentation accuracy and OP boundary precision and 2) less dependence on the scale parameter.

Defect detection is necessary before the use of industrial castings. DR (Digital Radiograph) system has a broad application prospect because of high detection efficiency. The CV model is an image segmentation method with high accuracy, but its segmentation result is not perfect in the case of strong edge. As a common image segmentation method, region growing has simple calculation as a result of using the space character of the image. In this paper, the defect detection about DR synthetic color image of railway truck side frame was conducted by CV model and the region growing methods, and segmentation achieved good results.

Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection .Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process .For the reason of picking up the contour’s convergent speed and enable the avoidance of trap .Four stages are constructed. Firstly, a median filtering is applied to smooth the original image and reduce parts of noise. Secondly, the usage of K-means algorithm gains more obvious differences of characteristics. Next, the reconstruction of gradient is obtained by using Sobel operator. Finally, segmentation result is achieved by using an improved method of multi-phase level set image segmentation. Experimental results show that the proposed approach has advantages in rapid and efficient application of remote sensing image segmentation.

The depth map is a basic diagram of the intrinsic; each pixel value represents the scene graph the elevation position of the object point. In this paper, the analysis methods for target classification elevation map. Figure elevation are visible depth image, the depth of the image is the distance from each point in the scene to the image capture device values ​​of the image as an image pixel value.