Morphological active contours for image segmentation have become popular due to their low
computational complexity coupled with their accurate approximation of the partial differential equations
involved in the energy minimization of the segmentation process. In this paper, a morphological active
contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is
coupled with a morphological edge-driven segmentation term to accurately segment natural images. By
using morphological approximations of the energy minimization steps, the algorithm has a low
computational complexity. Additionally, the coupling of the edge-based and region-based segmentation
techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and
robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and
report on the segmentation results using the Sorensen-Dice similarity coefficient
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that considers data fidelity, shape consistency, and elastic deformation. The functional is optimized using a minimum ratio cycle algorithm on graphics cards, allowing real-time segmentation and tracking of deformable objects while guaranteeing a globally optimal solution. The method can be extended to track multiple deformable anatomical structures in medical images.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that consists of three terms: data fidelity favoring strong edges, shape consistency favoring similar tangent angles, and an elastic penalty for stretching. Optimization is performed using simulated annealing for segmentation and iterated conditional modes for tracking. The algorithm provides optimal segmentation and point correspondences between template and image curve in linear time.
This document summarizes a research paper that proposes a new approach for tracking multiple deformable anatomical structures in medical images using geometrically deformable templates (GDTs). The GDTs can deform to match similar shapes based on image forces while minimizing a penalty function that measures deformation from the template's equilibrium shape. This allows simultaneous segmentation of multiple objects using intra- and inter-shape information. Simulated annealing is used for segmentation while iterated conditional modes is used for tracking. The paper also reviews previous work on image segmentation, tracking deformable objects, and shape-based image segmentation.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
Graph Theory Based Approach For Image Segmentation Using Wavelet TransformCSCJournals
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that considers data fidelity, shape consistency, and elastic deformation. The functional is optimized using a minimum ratio cycle algorithm on graphics cards, allowing real-time segmentation and tracking of deformable objects while guaranteeing a globally optimal solution. The method can be extended to track multiple deformable anatomical structures in medical images.
This document summarizes a method for tracking deformable objects in images. It proposes casting the problem as finding optimal cyclic paths in a product space of the template shape and input image. A cost functional is introduced that consists of three terms: data fidelity favoring strong edges, shape consistency favoring similar tangent angles, and an elastic penalty for stretching. Optimization is performed using simulated annealing for segmentation and iterated conditional modes for tracking. The algorithm provides optimal segmentation and point correspondences between template and image curve in linear time.
This document summarizes a research paper that proposes a new approach for tracking multiple deformable anatomical structures in medical images using geometrically deformable templates (GDTs). The GDTs can deform to match similar shapes based on image forces while minimizing a penalty function that measures deformation from the template's equilibrium shape. This allows simultaneous segmentation of multiple objects using intra- and inter-shape information. Simulated annealing is used for segmentation while iterated conditional modes is used for tracking. The paper also reviews previous work on image segmentation, tracking deformable objects, and shape-based image segmentation.
The document describes a method for tracking objects of deformable shapes in images. It proposes representing the matching of a deformable template to an image as a minimum cost cyclic path in a product space of the template and image. An energy functional is introduced that consists of a data term favoring strong image gradients, a shape consistency term favoring similar tangent angles, and an elastic penalty. Optimization is performed using a minimum ratio cycle algorithm parallelized on GPUs. This provides efficient, pixel-accurate segmentation and correspondence between template and image curve. The method can be extended to 4D to segment and track multiple deformable anatomical structures in medical images.
Graph Theory Based Approach For Image Segmentation Using Wavelet TransformCSCJournals
This paper presents the image segmentation approach based on graph theory and threshold. Amongst the various segmentation approaches, the graph theoretic approaches in image segmentation make the formulation of the problem more flexible and the computation more resourceful. The problem is modeled in terms of partitioning a graph into several sub-graphs; such that each of them represents a meaningful region in the image. The segmentation problem is then solved in a spatially discrete space by the well-organized tools from graph theory. After the literature review, the problem is formulated regarding graph representation of image and threshold function. The boundaries between the regions are determined as per the segmentation criteria and the segmented regions are labeled with random colors. In presented approach, the image is preprocessed by discrete wavelet transform and coherence filter before graph segmentation. The experiments are carried out on a number of natural images taken from Berkeley Image Database as well as synthetic images from online resources. The experiments are performed by using the wavelets of Haar, DB2, DB4, DB6 and DB8. The results are evaluated and compared by using the performance evaluation parameters like execution time, Performance Ratio, Peak Signal to Noise Ratio, Precision and Recall and obtained results are encouraging.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
ROLE OF HYBRID LEVEL SET IN FETAL CONTOUR EXTRACTIONsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
Role of Hybrid Level Set in Fetal Contour Extractionsipij
Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel predicate for active region merging in automatic image segmentationeSAT Journals
Abstract Image segmentation is an elementary task in computer vision and image processing. This paper deals with the automatic image segmentation in a region merging method. Two essential problems in a region merging algorithm: order of merging and the stopping criterion. These two problems are solved by a novel predicate which is described by the sequential probability ratio test and the minimal cost criterion. In this paper we propose an Active Region merging algorithm which utilizes the information acquired from perceiving edges in color images in L*a*b* color space. By means of color gradient recognition method, pixels with no edges are clustered and considered alone to recognize some preliminary portion of the input image. The color information along with a region growth map consisting of completely grown regions are used to perform an Active region merging method to combine regions with similar characteristics. Experiments on real natural images are performed to demonstrate the performance of the proposed Active region merging method. Index Terms: Adaptive threshold generation, CIE L*a*b* color gradient, region merging, Sequential Probability Ratio Test (SPRT).
An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
IMAGE SEGMENTATION BY MODIFIED MAP-ML ESTIMATIONScscpconf
This document summarizes an image segmentation algorithm called Modified MAP-ML Estimations. It begins with an abstract describing the algorithm and its benefits of faster execution time compared to existing algorithms. It then reviews related work in image segmentation techniques and their limitations. The document describes the probabilistic model used in the algorithm, which formulates segmentation as a labeling problem. It explains the MAP estimation approach used to estimate label configurations, and the ML estimation used to estimate region properties. The algorithm iterates between these two estimations to perform segmentation.
This document summarizes an image segmentation algorithm called Modified MAP-ML Estimations. It begins with an abstract describing the algorithm and its benefits of faster execution time compared to existing algorithms. It then reviews related work in image segmentation techniques and their limitations. The document describes the probabilistic model used in the algorithm, which formulates segmentation as a labeling problem. It explains the MAP estimation approach used to estimate label configurations, defining energy functions minimized through graph cuts. ML estimation is then used to update the region feature estimates in an iterative process. In summary, this algorithm modifies an existing MAP-ML approach to achieve comparable segmentation results to other algorithms, but in a faster execution time without human intervention.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based and region-based approaches. It then describes initializing a contour using region-based segmentation. The main contribution is a method that uses a convex active contour model incorporating both regional and boundary information to find a globally optimal segmentation. The model includes terms for regional color distributions estimated from user seeds and a boundary term to regularize the contour shape.
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based methods like active contours/snakes and region-based methods like random walks and graph cuts. The proposed method initializes a contour using region-based segmentation then refines it using a convex active contour model that incorporates both regional information from seed pixels and boundary smoothness. This allows the contour to globally evolve to object boundaries while handling topology changes.
This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant
Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the
features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a
pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in
the applications such as detection and classification.
Partitioning intensity inhomogeneity colour images via Saliency- based active...IJECEIAES
Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model.
Medial Axis Transformation based Skeletonzation of Image Patterns using Image...IOSR Journals
1) The document discusses extracting the medial axis transform (MAT) of an image pattern using the Euclidean distance transform. The image is first converted to binary, then the Euclidean distance transform is used to compute the distance of each non-zero pixel to the closest zero pixel.
2) The medial axis transform represents the core or skeleton of an image pattern. There are different algorithms for extracting the skeleton or medial axis, including sequential and parallel algorithms. The skeleton provides a simple representation that preserves topological and size characteristics of the original shape.
3) The document provides background on medial axis transforms and different skeletonization algorithms. It then describes preparing the binary image and applying the Euclidean distance transform to extract the MAT and skeleton
Implementation of Fuzzy Logic for the High-Resolution Remote Sensing Images w...IOSR Journals
This document describes an implementation of fuzzy logic for high-resolution remote sensing image classification with improved accuracy. It discusses using an object-based approach with fuzzy rules to classify urban land covers in a satellite image. The approach involves image segmentation using k-means clustering or ISODATA clustering. Features are then extracted from the image objects and fuzzy logic is applied to classify the objects based on membership functions. The method was tested on different sensor and resolution images in MATLAB and showed improved classification accuracy over other techniques, achieving lower entropy in results. Future work planned includes designing an unsupervised classification model combining k-means clustering and fuzzy-based object orientation.
This document summarizes a research paper that proposes a novel approach for enhancing digital images using morphological operators. The approach aims to improve contrast in images with poor lighting conditions. It uses two morphological operators based on Weber's law - the first employs blocked analysis while the second uses opening by reconstruction to define a multi-background. The performance of the proposed operators is evaluated on images with various backgrounds and lighting conditions. Key steps include dividing images into blocks, estimating minimum/maximum intensities in each block to determine background criteria, and applying contrast enhancement transformations based on the criteria. Opening by reconstruction is also used to approximate image background without modifying structures. Experimental results demonstrate the approach enhances images with poor lighting.
Object recognition is the challenging problem in the real world application. Object recognition can be achieved through the shape matching. Shape matching is preceded by i) detecting the edges of the objects from the images. ii) Finding the correspondence between the shapes. iii) Measuring the dissimilarity between the shapes using the correspondence. iv) Classifying the object into classes by using this dissimilarity measures. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The one to one correspondence is achieved through the cost based bipartite graph matching. The cost matrix is reduced through Hungarian algorithm. The dissimilarity between the two shapes is computed using Canberra distance. The nearest neighbor classifier is used to classify the objects with the matching error. The results are obtained using the MATLAB for MINIST hand written digits.
This document describes a narrow band region approach for 2D and 3D image segmentation using deformable curves and surfaces. Specifically, it develops a region energy term involving a fixed-width band around the evolving curve or surface. This energy achieves a balance between local gradient features and global region statistics. The region energy is formulated to allow efficient computation on explicit parametric models and implicit level set models. Two different region terms are introduced, each suited to different configurations of the target object and its surroundings. The document derives the mathematical framework for computing the region energies and their gradients to allow minimization via gradient descent. It then discusses numerical implementations and provides experiments segmenting medical and natural images.
6 superpixels using morphology for rock imageAlok Padole
The document describes a new superpixel segmentation algorithm called Superpixels Using Morphology (SUM) and compares it to existing algorithms. SUM uses a watershed transformation approach on an image's morphological gradient that has undergone area closing to efficiently generate superpixels. In experiments on rock images, SUM achieved under-segmentation error and boundary recall comparable to recent algorithms while being significantly faster, making it suitable for applications requiring fast superpixel generation.
Local Phase Oriented Structure Tensor To Segment Texture Images With Intensit...CSCJournals
This paper proposed the active contour based texture image segmentation scheme using the linear structure tensor and tensor oriented steerable Quadrature filter. Linear Structure tensor (LST) is a popular method for the unsupervised texture image segmentation where LST contains only horizontal and vertical orientation information but lake in other orientation information and also in the image intensity information on which active contour is dependent. Therefore in this paper, LST is modified by adding intensity information from tensor oriented structure tensor to enhance the orientation information. In the proposed model, these phases oriented features are utilized as an external force in the region based active contour model (ACM) to segment the texture images having intensity inhomogeneity and noisy images. To validate the results of the proposed model, quantitative analysis is also shown in terms of accuracy using a Berkeley image database.
Factors affecting undergraduate students’ motivation at a university in Tra VinhAJHSSR Journal
ABSTRACT: Motivation plays an important role in foreign language learning process. This study aimed to
investigate student’s motivation patterns towards English language learning at a University in Tra Vinh, and factors
affecting their motivation change toward English language learning of non-English-major students in the semester.
The researcher used semi-structured interview at the first phase of choosing the participants and writing reflection
through the instrument called “My English Learning Motivation History” adapted from Sawyer (2007) to collect
qualitative data within 15 weeks. The participants consisted of nine first year non-English-major students who learning
General English at pre-intermediate level. They were chosen and divided into three groups of three members each
(high motivation group; average motivation group; and low motivation group). The results of the present study
identified six visual motivation patterns of three groups of students with different motivation fluctuation, through the
use of cluster analysis. The study also indicated a diversity of factors affecting students’ motivation involving internal
factors as influencing factors (cognitive, psychology, and emotion) and external factors as social factors (instructor,
peers, family, and learning environment) during English language learning in a period of 15 weeks. The findings of
the study helped teacher understand relationship of motivation change and its influential factors. Furthermore, the
findings also inspired next research about motivation development in learning English process.
KEY WORDS: language learning motivation, motivation change, motivation patterns, influential factors, students’
motivation.
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Image processing technologies may be employed for quicker and accurate diagnosis in analysis and
feature extraction of medical images. Here, existing level set algorithm is modified and it is employed for
extracting contour of fetus in an image. In traditional approach, fetal parameters are extracted manually
from ultrasound images. An automatic technique is highly desirable to obtain fetal biometric measurements
due to some problems in traditional approach such as lack of consistency and accuracy. The proposed
approach utilizes global & local region information for fetal contour extraction from ultrasonic images.
The main goal of this research is to develop a new methodology to aid the analysis and feature extraction.
A novel predicate for active region merging in automatic image segmentationeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A novel predicate for active region merging in automatic image segmentationeSAT Journals
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An efficient image segmentation approach through enhanced watershed algorithmAlexander Decker
This document proposes an efficient image segmentation approach combining an enhanced watershed algorithm and color histogram analysis. The watershed algorithm is applied to preprocessed images after merging the results with an enhanced edge detection. Over-segmentation issues are addressed through a post-processing step applying color histogram analysis to each segmented region, improving overall performance. The document provides background on image segmentation techniques, reviews related work applying watershed algorithms, and discusses challenges like over-segmentation that watershed approaches can face.
IMAGE SEGMENTATION BY MODIFIED MAP-ML ESTIMATIONScscpconf
This document summarizes an image segmentation algorithm called Modified MAP-ML Estimations. It begins with an abstract describing the algorithm and its benefits of faster execution time compared to existing algorithms. It then reviews related work in image segmentation techniques and their limitations. The document describes the probabilistic model used in the algorithm, which formulates segmentation as a labeling problem. It explains the MAP estimation approach used to estimate label configurations, and the ML estimation used to estimate region properties. The algorithm iterates between these two estimations to perform segmentation.
This document summarizes an image segmentation algorithm called Modified MAP-ML Estimations. It begins with an abstract describing the algorithm and its benefits of faster execution time compared to existing algorithms. It then reviews related work in image segmentation techniques and their limitations. The document describes the probabilistic model used in the algorithm, which formulates segmentation as a labeling problem. It explains the MAP estimation approach used to estimate label configurations, defining energy functions minimized through graph cuts. ML estimation is then used to update the region feature estimates in an iterative process. In summary, this algorithm modifies an existing MAP-ML approach to achieve comparable segmentation results to other algorithms, but in a faster execution time without human intervention.
Image segmentation by modified map ml estimationsijesajournal
Though numerous algorithms exist to perform image segmentation there are several issues
related to execution time of these algorithm. Image Segmentation is nothing but label relabeling
problem under probability framework. To estimate the label configuration, an iterative
optimization scheme is implemented to alternately carry out the maximum a posteriori (MAP)
estimation and the maximum likelihood (ML) estimations. In this paper this technique is
modified in such a way so that it performs segmentation within stipulated time period. The
extensive experiments shows that the results obtained are comparable with existing algorithms.
This algorithm performs faster execution than the existing algorithm to give automatic
segmentation without any human intervention. Its result match image edges very closer to
human perception.
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based and region-based approaches. It then describes initializing a contour using region-based segmentation. The main contribution is a method that uses a convex active contour model incorporating both regional and boundary information to find a globally optimal segmentation. The model includes terms for regional color distributions estimated from user seeds and a boundary term to regularize the contour shape.
This document presents a method for interactive image segmentation using constrained active contours. It begins with an overview of existing interactive segmentation techniques, including boundary-based methods like active contours/snakes and region-based methods like random walks and graph cuts. The proposed method initializes a contour using region-based segmentation then refines it using a convex active contour model that incorporates both regional information from seed pixels and boundary smoothness. This allows the contour to globally evolve to object boundaries while handling topology changes.
This project is to retrieve the similar geographic images from the dataset based on the features extracted.
Retrieval is the process of collecting the relevant images from the dataset which contains more number of
images. Initially the preprocessing step is performed in order to remove noise occurred in input image with
the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant
Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the
features from the images. After this process, the relevant geographic images are retrieved from the dataset
by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which
are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to
perform reliable recognition, it is important that the feature extracted from the training image be
detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a
pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in
the applications such as detection and classification.
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This document describes a narrow band region approach for 2D and 3D image segmentation using deformable curves and surfaces. Specifically, it develops a region energy term involving a fixed-width band around the evolving curve or surface. This energy achieves a balance between local gradient features and global region statistics. The region energy is formulated to allow efficient computation on explicit parametric models and implicit level set models. Two different region terms are introduced, each suited to different configurations of the target object and its surroundings. The document derives the mathematical framework for computing the region energies and their gradients to allow minimization via gradient descent. It then discusses numerical implementations and provides experiments segmenting medical and natural images.
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A HYBRID MORPHOLOGICAL ACTIVE CONTOUR FOR NATURAL IMAGES
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
DOI : 10.5121/ijcsea.2013.3401 1
A HYBRID MORPHOLOGICAL ACTIVE CONTOUR
FOR NATURAL IMAGES
Victoria L. Fox1
, Mariofana Milanova2
, Salim Al-Ali3
1
Department of Applied Science, University of Arkansas at Little Rock, USA
vlfox@ualr.edu
2
Department of Computer Science, University of Arkansas at Little Rock, USA
mgmilanova@ualr.edu
3
Department of Computer Science, University of Arkansas at Little Rock, USA
sgsaeed@ualr.edu
ABSTRACT
Morphological active contours for image segmentation have become popular due to their low
computational complexity coupled with their accurate approximation of the partial differential equations
involved in the energy minimization of the segmentation process. In this paper, a morphological active
contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is
coupled with a morphological edge-driven segmentation term to accurately segment natural images. By
using morphological approximations of the energy minimization steps, the algorithm has a low
computational complexity. Additionally, the coupling of the edge-based and region-based segmentation
techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and
robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and
report on the segmentation results using the Sorensen-Dice similarity coefficient.
KEYWORDS
Natural Images, Segmentation,Hybrid Contour, Morphology, Active Contours, Object Extraction
1. INTRODUCTION
One goal of natural image segmentation is to accurately mimic the object recognition and scene
analysis of human visual perception.While organic perceptual systems efficiently segment images
into homogenous regions [1], obtaining similar results is a challenging problem for computational
image segmentation. The difficulty in computational processing of natural images can be
attributed to inherent statistical complexities of the image and a lack of homogeneity or saliency
of local features at the same spatial or quantization scale [2]. As a result, segmentation of natural
images remains a challenging task in image processing and computer vision.In recent literature,
methods for segmentation of natural images include active contours [3-5], clustering methods [6-
9],lossy data compression [10, 11] and graph cuts [12].
1.1. Active Contour Models
In this work, we are interested in unsupervised, active contour models for segmentation. The
method of image segmentation by active contours can be divided into three approaches: edge
based, region based, and hybrid. Edge based active contours represented by [4]and the references
therein use an energy driven by attraction to the edges of regions of interest in the image. For
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
2
accuracy, the object of interest should have obvious boundaries usually represented by a dramatic
change in the gradient values of the image. In the case of natural images, edges of the object of
interest are not always clearly delineated and edge based methods often require more complex
algorithms to achieve some degree of accuracy in the segmentation. Additionally, edge-based
algorithms must be carefully formulated so that image gradient changes due to lighting,
background objects, and noise do not cause the contour to inaccurately segment the image. This
requirement also adds to the complexity of edge-based segmentation models.
Region based segmentation models [5, 13-18] use image statistics to partition the image into
regions. Ideally, the object of interest will have different statistics in comparison to surrounding
areas and can then be quickly segmented. As is often the case, however, natural images have a
vast number of visual and textural patterns and are inherently noisy. Traditional methods work to
resolve the problem by incorporating local statistics in the contour evolution [16-18] which has
led to a new set of issues regarding the accuracy of the segmentation. While accuracy is greatly
improved when statistical models include local information, these methods often still fail to fit the
contour to the object boundaries due to a lack of edge information and they are sensitive to the
size of the window used to calculate local statistics [15]. Other region based segmentation
algorithms frequently incorporate some type of texture analysis, e.g. [13] and its references, into a
pre-processing step prior to region segmentation. While the texture analysis greatly enhances the
accuracy of the model, it also contributes to the complexity and computational cost.
More recently, hybrid models which incorporate both edge and region information in their
segmentation algorithms have been introduced [19-23]. In hybrid models, a strong edge term
attracts the curve toward objects of interest while a region-based term propagates the curve when
the edge information is absent. While hybrid methods have found success in medical
segmentation applications [19, 21-22], there is a lack of literature involving the accuracy of
hybrid methods with respect to natural images. To the authors’ knowledge, while [23] mentions
natural image segmentation and provides a simple example, a robust study of the effectiveness of
a hybrid strategy for natural image segmentation has not been conducted. This may be due to the
shortcomings of both methods in conjunction with the intensity inhomogeneity and statistical
complexity of natural images does not allow an efficient, robust algorithm when attempted with
traditional hybrid methods. If one was to use a hybrid active contour method for natural image
segmentation, it could be construed that it would have to be a method which uses a simpler
computational scheme than the statistical and energy minimization schemes of regional and edge
based methods while enhancing the accuracy of the segmentation.
1.2. Morphological Active Contours
One method of morphological segmentation is achieved through repeated median filtering with an
adaptive structuring element which has been shown to be a self snake [24]. While
mathematically sound, the process in itself has a high computational cost since it is achieved by
filtering pixel-by-pixel. A less computationally expensive method of segmentation through
morphological structuring elements is the proven practice of using morphology to approximate
the partial differential equations used in traditional active contour models. The morphological
evolution solves the traditional partial differential equations but avoids many issues associated
with numerical algorithms.
In edge based active contour methods, the contour is composed of three components: a balloon
force, a smoothing force, and an edge attraction force. A region based active contour typically has
three terms as well: a balloon force, a statistical region force, and a smoothing force. For the
morphological approximation of the forces, it is well known that erosion or dilation will
approximate the balloon force of the formulation. The edge attractant force and region forces can
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
3
be approximated discretelyby comparison of image gradients and region statistics, respectively.
However, until recently, there was not a morphological equivalent to the smoothing term and
pseudo-morphological active contours still used partial differential equations to smooth the
contour.
In partial differential equation formulations of active contours, the smoothing force regularly
takes the form of mean curvature motion and acts as a regularization term. The underlying
principal of mean curvature motion is the evolution of a simple closed curve whose points move
in the direction of the normal with specified velocity.
Figure 1: Mean Curvature Motion
Catte, Dibos, and Koepfler [25] advanced the topic of creating a discrete smoothing force by
proving the two-dimensional mean curvature term can be replaced by the mean of two
morphological operators for a single iteration of the method. To do this, we let represent line
segments of set length then define the morphological continuous line operators as
( )( ) =
∈ ∈ +
( ), (1)
( )( ) =
∈ ∈ + ( ). (2)
Using these operators, we thendefine the mean operator as
( )( ) =
( )+ ( )
2
(3)
in which the scheme in [25] relates the mean operator with the mean curvature motion by
( )( ) = ( ) + .25 |∇ |
| |
( ) + ( ). (4)
Using a small h and subtracting u(x) from each side of (4) results in the infinitesimal generator of
the operator:
lim → √
( ) − ( ) = | |
| |
( ). (5)
From (5) we can solve the mean curvature motion by means of the operator. However, since
the operator generates new level set values after a single iteration, it ceases to be
morphological. In [26] and [27], Alarez et al. modify the Catte, Dibos, and Koepfler scheme with
the use of operator composition which states that given any two operators and , we have,
for a small h,
≈
2
+ 1
2
. (6)
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
4
From this, Alarez et al. show that the non-morphological operator √ can be approximated by
the morphological operator √ √ with a base of . With this morphological operator in
place, it is now possible to develop a discrete morphological active contour for edge based and
region based active contours.Results of using discrete, morphological active contours in [26] and
[27] show a much lower computational cost and a subsequent increase in computational speed.
Unfortunately, approaching segmentation of natural images by a solely edge-based or region-
based morphological active contour does not result in any higher accuracyin segmentation results.
For example, the natural images used in [27]– of which there are two – still show the
segmentation flaws of their partial differential equation counterparts. Despite this, the
contributions of Alarez and his co-authors are extremely significant due to the mathematical
validation of a morphological mean curvature approximation and the formulation of discrete,
morphological algorithms for both edge based and region based methods.
1.3. Contribution
In our approach, we contend that a more accurate morphological active contour for natural images
can be obtained if we combine positives of the edge-based methods with region statistics. The
proposed method uses the morphological equivalent of an edge-driven balloon force coupled with
region statistics and morphological mean curvature motion to segment natural images. The use of
morphological operators results in an efficient implementation of the algorithm while the hybrid
contour accurately segments natural images when compared to ground-truth. Using the
Sorenson-Dice similarity coefficient to evaluate accuracy, we found our segmentation of the
images provided by the Weizmann Segmentation Evaluation Database averaged a 0.9302
similarity ratio. In short, the proposed method is simple, efficient, and robust for the
segmentation of natural images. Additionally, the use of morphological operators and edge-based
balloon forces resulted in accurate segmentation of multiphase images in the dataset.
2. HYBRID MORPHOLOGICAL ACTIVE CONTOUR
The idea to combine edge and region information in a segmentation algorithm is not new.
Usually, the combination results in increased computational complexity to mitigate the
shortcomings of either edge based or region based segmentation or both[15]. The advantage of a
hybrid morphological contour is both the edge based and region based methods have low
complexity and circumventing a method’s shortcoming does not significantly add to the
computational cost. For example, in order to control illumination issues or noise, one need only
alter the structuring element in either the balloon force or smoothing term. The hybrid method
uses a zero level set of a binary piecewise constant function u: → {0,1}. The morphological
operators act on u and implicitly evolve the curve.
2.1. Balloon Force
The edge-based portion of the method is represented by an edge-driven balloon force. In edge
based methods, the contourflow is often represented with
= ( )|∇ | + ∇ ( )∇ + ( )|∇ |
∇
|∇ |
(7)
where ( )|∇ | is the balloon force, ∇ ( )∇ is the edge attraction force, and
( )|∇ |
∇
|∇ |
is mean curvature motion. ( )represents an edge image obtained from an
edge detector, udenotes the contour, and vis an inflation (or deflation) constant. Focusing on the
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
5
balloon force, ( ) could be obtained from any edge detector appropriate for the image.
Traditionally, one would use an edge detector which is low in the edges of the image such as
( ) =
1
1+ ∇ ∗
. (8)
In the proposed method, morphological operations of dilation and erosion are used to
approximate the balloon force. The dilation of a function is defined as ( )( ) =
∈ ( − ) while erosion is defined by ( )( ) = ∈ ( − ). The radius of the
operator is denoted by h and B is a disk structuring element of radius one.The function : ×
→ where ( , ) = ( ) is the solution to
= |∇ | (9)
for the initial condition (0, ) = ( ) [28]. As a result, is the infinitesimal generator of
(9). Using a comparable rational, we have the function : × → where ( , ) =
( ) is the solution to
= −|∇ | (10)
withinitial condition (0, ) = ( ). Using the morphological operators and , we can
now solve level set evolution PDEs like equations 9 and 10. In the balloon force term, ( )
manages the balloon force in individual sections of the curve. The smaller ( ) becomes, the
closer the curve is to the edge. With the use of a threshold, factor ( ) can be discretized into the
morphological formulation. The product | | leads to equations the PDES in equations 9 and
10. If is positive, the PDE becomes the dilation PDE. Likewise, if is negative, then the
erosion PDE is used. Therefore, the solution to the balloon force PDE applied over the contour
: → {0,1} can determined with the following morphological method using discrete dilation,
and discrete erosion :
( ) =
( )( )if ( )( ) > and > 0
( )( )if ( )( ) > and < 0
otherwise .
(11)
2.2. Region Statistics
In the proposed hybrid method, the coupling of the strong edge term and region statistics creates a
symbiotic relationship. When the edge term is low, the curve is attracted toward the region of
interest. However, when the curve is far away from an edge, the region statistics take control of
the curve evolution and the contour resists becoming a stationary model. While there are several
different statistics that can be of use in region based segmentation, the use of intensity statistics
provided accurate and efficient guidance for our segmentation experiments.
A common region based model, the Chan-Vese Active Contour without Edges [29], gives the
following functional of a curve :
( , , ) = ( ) + ( ) + ∫ | − |
+ ∫ | − |
6. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
6
where and are the average intensity levels inside and outside the contour. The constant
penalizes the length of the contour while limits the area inside the contour. Parameters and
weight the importance of the regions inside and outside the curve, respectively. To ease
computation of the minimization of the functional, it is noted that
{ ≥ 0} = ∫ ( ) (12)
in which ( ) is a signed, step function known as the Heaviside function and Ω is the image
domain. The impact of equation 12 for the intensity terms of the Chan-Vese function is
demonstrated in the following equations:
∫ | − | = ∫ | − | = ∫ | − | ( ) (13)
∫ | − | = ∫ | − | = ∫ | − | (1 − ( )) (14)
In short, the intensity terms can now be computed using the image domain rather than the region
domain.
In [27], it is shown that the Active Contours without Edges functional can be minimized with a
morphological active contour. Once again, the functional is comprised of three energies: balloon
force, region force, and a smoothing force. The balloon force is represented by ( ), the
region force is denoted by ( ). The morphological equivalence of the balloon force
is given by
( ) =
( )( ) > 0
( )( )p < 0
otherwise .
(15)
The authors of [27] did not use the Heaviside equation in the calculation of the region statistics.
Using the image intensity terms from the original Chan-Vese equation, the region term can be
discretely modelled by
=
1 |∇ |[( ( − ) − ( − ) ]( ) < 0
0 | |[( ( − ) − ( − ) ]( ) > 0 (16)
where and are the mean of the values of intensity inside and outside the contour. However, in
terms of computational stability and robustness, the use of equations 13 and 14 in our proposed
hybrid method greatly increase the accuracy of the method. As a result, our region statistics term
uses the following to compute and :
=
∫ ∗ ( )
∫
(17)
=
∫ ∗ ( )
∫
(18)
2.3. Mean Curvature Motion
The smoothing term in the hybrid morphological active contour takes the form of the
morphological equivalent of mean curvature motion. Recall from the geodesic active contour
7. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
7
method expressed in equation 7, the mean curvature motion is given by ( )|∇ |
∇
|∇ |
.
Using equations 5 and 6 and a simple threshold for ( ), the morphological evolution of the mean
curvature motion is
=
( ) ( ) ( )( ) >
.
(19)
Both and have a discrete version of , denoted , which is a collection of four discretized
segments centered at the origin of a set length. A natural explanation of the morphological mean
curvature motion operator involves applying it to a binary image. In a binary image, works
only on white pixels and works on black pixels. Suppose ( ) is a black pixel. Then
∈ ( ) will be zero for every segment Lin . Similarly, does not affect white pixels.
For every white pixel, , in a binary image, the operator looks for line segments of a set pixel
length which cover . This search is done in an eight-connected component pattern. If no line
exists, then the pixel is made black. For black pixels, performs similarly. The composition
first eliminates unconnected black pixels with and then repeats the procedure for
white pixels with . The result is a smoothing of the contour for eachrepetition of the
algorithm.
3. IMPLEMENTATION
The hybrid morphological active contour is comprised of two edge based terms and one region
based term. Using an edge attraction term, region statistics with and computed with the
Heaviside function, and the morphological mean curvature motion, we can formulate the active
contour algorithm.
3.1. Algorithm
In PDE based active contours, the balloon, image attraction, and smoothing forces are combined
through addition of the terms. Our hybrid morphological active contour will combine them by
iteratively interchanging their discretized formulations. In every iteration, we will first apply the
balloon force with the edge attraction energy (11), then apply the region force (16), and finish
with the mean curvature motion (19) over the embedded level set function . Given the contour
evolution at iteration , : → {0, 1}, we define using the following steps:
Step 1: ( ) =
( )( )if ( )( ) > and > 0
( )( )if ( )( ) > and < 0
otherwise .
(20)
Step 2: =
1 ∇ [( ( − ) − ( − ) ]( ) < 0
0 [( ( − ) − ( − ) ]( ) > 0
( )
where =
∫ ∗ ( )
∫
and =
∫ ∗ ( )
∫
Step 3: =
( ) ( ) ( )( ) >
( ) .
8. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
8
which is the morphological formulation of the hybrid active contour PDE. The constant in Step
1 allows us to control the balloon force separate from the smoothing operator. From this, we can
directly control the strength of the balloon operator.
3.2. Implementation Details
The execution of (20) is trivial, but some specifics regarding the details of the implementation are
worth mentioning. First and foremost, regarding the edge detector, our experiments use the built-
in Matlab function edge(Img, ‘canny’,[thresh1 thresh2],sigma). The input image is a RGB color
image, thresh1=0.2, thresh2=0.6, and sigma=1. We maintained these settings for all of our
experimental results. The Canny edge detector finds edges by looking for local maxima of the
gradient of the image which is calculated using the derivative of a Gaussian filter. Thresh1 and
thresh2 are used to detect strong and weak edges. Weak edges are only included in the edge
image if they are connected to strong edges. Sigma is used for the Gaussian kernel.
Also, while it would be simple to extend the morphological operations to grayscale values, the
morphological operations used in our implementation are defined as binary operations. The
embedded level set is also defined as a binary function. As such, the structuring element for a 3
by 3 window in the mean curvature motion is a square of values of 1.
(−1, −1), (0,0) , (1,1)
(−1,1) , (0,0), (1, −1)
(0, −1), (0,0), (0,1)
(−1,0)(0,0)(1,0)
=
Figure 2: Morphological mean curvature structuring element and associated discrete line
segments.
It is not outside the realm of possibility to use line segments of greater length. However, to
maintain experimental integrity we use the structuring element in Figure 2 to obtain all of our
experimental results.
The region statistics areacquired from the RGB color image rather than its grayscale counterpart.
The segmentation algorithm is ran as an unsupervised method in which = ( ), = = 1,
and = −1. The threshold for the balloon force and the mean curvature motion is set to 0.8∗
( ). Finally, |∇ | is approximated by the magnitude of the gradient, namely + where
and are computed using finite differences.
3.3. Experimental Results
Using the natural images available at the Weizmann Segmentation Evaluation Database [30], we
performed our algorithm for the images and computed a Sorensen-Dice similarity coefficientfor
each image when compared to the provided image ground truth. The average over the
segmentation of the data set was 0.9302 which, to the authors’ knowledge, is the best Dice score
in the literature regarding natural image segmentation. Adjustment of the parameters would
naturally have led to better segmentation results for the images which segmented under a 0.9700
similarity coefficient.Table 1 lists statistical information regarding the segmentation of the 204
images in the dataset – 102 images for one object segmentation and 102 images for two object
segmentation – while Table 2 gives the segmentation results and Sorensen-Dice coefficients for
1 1 1
1 1 1
1 1 1
9. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
9
twelve randomly selected segmented images. Figure 3 demonstrates the frequency of the
Sorensen-Dice coefficients for the 204 images.
Table 1: Sorensen-Dice Coefficient Results
Sorensen-Dice
Similarity
Coefficient
1 Object
Segmentation
2 Object
Segmentation
Minimum 0.6884 0.4813
Maximum 0.9999 0.9999
Median 0.9582 0.9692
Mean 0.9253 0.9153
Figure 3: Frequency of Sorensen-Dice Similarity Coefficient for segmentations of 204 images in the
dataset when compared to the dataset ground truth.
1 1
7
19 17 19
25
115
0
20
40
60
80
100
120
0 to
0.5000
0.5001
to
0.7000
0.7001
to
0.7500
0.7501
to
0.8000
0.8001
to
0.8500
0.8501
to
0.9000
0.9001
to
0.9500
0.9501
to
0.9999
Segmentations
Sorensen-Dice Coefficient
Frequency of Sorensen-Dice Coefficients for
Segmentations
10. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
10
Table 2: Segmentation Results of Twelve Randomly Selected Images
RGB Image Segmentation Dice
RGB Image
Segmentatio
n
Dice
0.985 0.999
0.988 0.987
0.982 0.986
0.973 0.983
0.994 0.961
0.986 0.955
As an example of images in which the proposed method with the stated parameters did not
perform well, Table 3 lists the two worst segmentation results from the dataset. While both of the
segmentations in Table 3 would have benefitted from parameter tuning, they demonstrate two
distinct shortcomings of the method.
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
10
Table 2: Segmentation Results of Twelve Randomly Selected Images
RGB Image Segmentation Dice
RGB Image
Segmentatio
n
Dice
0.985 0.999
0.988 0.987
0.982 0.986
0.973 0.983
0.994 0.961
0.986 0.955
As an example of images in which the proposed method with the stated parameters did not
perform well, Table 3 lists the two worst segmentation results from the dataset. While both of the
segmentations in Table 3 would have benefitted from parameter tuning, they demonstrate two
distinct shortcomings of the method.
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
10
Table 2: Segmentation Results of Twelve Randomly Selected Images
RGB Image Segmentation Dice
RGB Image
Segmentatio
n
Dice
0.985 0.999
0.988 0.987
0.982 0.986
0.973 0.983
0.994 0.961
0.986 0.955
As an example of images in which the proposed method with the stated parameters did not
perform well, Table 3 lists the two worst segmentation results from the dataset. While both of the
segmentations in Table 3 would have benefitted from parameter tuning, they demonstrate two
distinct shortcomings of the method.
11. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
11
Table 3: Two worst segmentation results from the dataset
RGB Image Segmentation Dice Score
0.688
0.481
The first image has a clear area of textural homogeneity divided by a sharp intensity difference.
As a result the edge term and region term gravitated to the regions with a homogenous intensity.
In the second image, there is a clear textural difference between the foreground and background.
However, the edges of the region of interest are weak due to the intensity homogeneity across the
entire image. As a result, the edge term did not greatly affect the segmentation of the image. To
counteract both of these shortcomings, the method could use textural image statistics rather than
or in conjunction with intensity statistics. However, incorporating complex region statisticscould
negatively impact the overall computation cost of the algorithm.
4. CONCLUSIONS
This paper introduces a hybrid morphological active contour model. Based on the edge attraction
term and mean curvature motion of the Geodesic Active Contour and the region statistics model
of the Chan-Vese Active Contour without Edges, the hybrid method accurately and efficiently
segmented the natural images in the Weizmann Segmentation Evaluation Dataset. While a few of
the dataset images did not segment well with the static parameter settings used for the
experiment, the overall Sorenson-Dice similarity coefficient was 0.9302, the highest, to the
authors’ knowledge, similarity coefficient score for segmentation of natural images in a dataset.
Additionally, manual modification of the parameters would yield better segmentation for images
that segmented at a lower similarity ratio.
The experiments we have conducted are very promising. The flexibility of the method in the
parameter settings and types of statistics that could be incorporated in the region portion of the
algorithm could potentially allow for the segmentation of images with sharp intensity gradients
across regions of interest or weak edges in areas of intensity homogeneity. In future work, we
will experiment with other region statistics for segmentation and extend the method to
multispectral images.
ACKNOWLEDGEMENTS
The authors would like thank the anonymous reviewers for their comments and constructive
criticism of this work. Additionally, the authors would like to thank the contributors to the
Weizmann Segmentation Evaluation Dataset for the public use of their images and ground-truth
segmentations.
12. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
12
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[19] Lankton, Shawn, Delphine Nain, Anthony Yezzi, and Allen Tannenbaum. "Hybrid geodesic region-
based curve evolutions for image segmentation."In Medical Imaging, pp. 65104U-
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[21] Kamalakannan, S., S. Antani, R. Long, and G. Thoma. "Customized hybrid level sets for automatic
lung segmentation in chest x-ray images." In SPIE Medical Imaging, pp. 866939-866939.International
Society for Optics and Photonics, 2013.
[22] Ma, Liyan, and Jian Yu. "An unconstrained hybrid active contour model for image segmentation." In
Signal Processing (ICSP), 2010 IEEE 10th International Conference on, pp. 1098-1101. IEEE, 2010.
[23] Ali, Sahirzeeshan, and AnantMadabhushi. "Segmenting multiple overlapping objects via a hybrid
active contour model incorporating shape priors: applications to digital pathology."In SPIE Medical
Imaging, pp. 79622W-79622W.International Society for Optics and Photonics, 2011.
[24] Welk, Martin, Michael Breuß, and Oliver Vogel. "Morphological amoebas are self-snakes." Journal
of Mathematical Imaging and Vision 39.2 (2011): 87-99.
[25] Catté, Francine, Françoise Dibos, and Georges Koepfler. "A morphological scheme for mean
curvature motion and applications to anisotropic diffusion and motion of level sets."SIAM Journal on
Numerical Analysis 32.6 (1995): 1895-1909.
[26] Álvarez, Luis, Luis Baumela, Pedro Henríquez, and Pablo Márquez-Neila. "Morphological snakes."
In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2197-2202.
IEEE, 2010.
[27] Marquez-Neila, Pablo, Baumela, Luis, Alvarez, Luis. “A Morphological Approach to Curvature-
based Evolution of Curves and Surfaces.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.106, June 2013.
[28] Kimmel, Ron. Numerical geometry of images: Theory, algorithms, and applications. Springer, 2004.
[29] T. Chan and L. Vese, “Active contours without edges,” IEEE Transactions on Image Processing,
10(2):266-277, 2001.
[30] Alpert, Sharon, MeiravGalun, Ronen Basri, and Achi Brandt. "Image segmentation by probabilistic
bottom-up aggregation and cue integration."In Computer Vision and Pattern Recognition,
2007.CVPR'07. IEEE Conference on, pp. 1-8. IEEE, 2007.
AUTHORS
Currently a graduate student in Computational Science at University of Arkansas at
Little Rock, Victoria L. Fox is also a mathematics instructor for the University of
Arkansas at Monticello. Her professional interests include morphological image
processing, segmentation of natural images, and the incorporation of fuzzy logic in
multispectral image segmentation.
MariofonnaMilanova is a Professor of Computer Science Department at the University
of Arkansas at Little Rock since 2001.She received her M. Sc. degree in Expert
Systems and AI in 1991 and her Ph.D. degree in Computer Science in 1995 from the
Technical University, Sofia, Bulgaria. Dr.Milanova did her post-doctoral research in
visual perception at the University of Paderborn, Germany. She has extensive
academic experience at various academic and research organizations in different
countries. Milanova serves as a book editor of two books and associate editor of
several international journals. Her main research interests are in the areas of artificial intelligence,
biomedical signal processing and computational neuroscience, computer vision and communications,
machine learning, and privacy and security based on biometric research. She has published and co-
authored more than 70 publications, over 43 journal papers, 7 book chapters, numerous conference papers
and 2 patents.
Salim Al-Ali is a Ph.D. graduate student in the integrated computing of computer
science department at University of Arkansas at Little Rock (UALR). He received a
master degree from computer science department, Baghdad University, Iraq on 1995. He
is working as a teacher in Dohuk Technical Institute at Dohuk Polytechnic University.
His research interest field is computer vision in general, human action recognition,
image and video understanding.
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
13
[21] Kamalakannan, S., S. Antani, R. Long, and G. Thoma. "Customized hybrid level sets for automatic
lung segmentation in chest x-ray images." In SPIE Medical Imaging, pp. 866939-866939.International
Society for Optics and Photonics, 2013.
[22] Ma, Liyan, and Jian Yu. "An unconstrained hybrid active contour model for image segmentation." In
Signal Processing (ICSP), 2010 IEEE 10th International Conference on, pp. 1098-1101. IEEE, 2010.
[23] Ali, Sahirzeeshan, and AnantMadabhushi. "Segmenting multiple overlapping objects via a hybrid
active contour model incorporating shape priors: applications to digital pathology."In SPIE Medical
Imaging, pp. 79622W-79622W.International Society for Optics and Photonics, 2011.
[24] Welk, Martin, Michael Breuß, and Oliver Vogel. "Morphological amoebas are self-snakes." Journal
of Mathematical Imaging and Vision 39.2 (2011): 87-99.
[25] Catté, Francine, Françoise Dibos, and Georges Koepfler. "A morphological scheme for mean
curvature motion and applications to anisotropic diffusion and motion of level sets."SIAM Journal on
Numerical Analysis 32.6 (1995): 1895-1909.
[26] Álvarez, Luis, Luis Baumela, Pedro Henríquez, and Pablo Márquez-Neila. "Morphological snakes."
In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2197-2202.
IEEE, 2010.
[27] Marquez-Neila, Pablo, Baumela, Luis, Alvarez, Luis. “A Morphological Approach to Curvature-
based Evolution of Curves and Surfaces.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.106, June 2013.
[28] Kimmel, Ron. Numerical geometry of images: Theory, algorithms, and applications. Springer, 2004.
[29] T. Chan and L. Vese, “Active contours without edges,” IEEE Transactions on Image Processing,
10(2):266-277, 2001.
[30] Alpert, Sharon, MeiravGalun, Ronen Basri, and Achi Brandt. "Image segmentation by probabilistic
bottom-up aggregation and cue integration."In Computer Vision and Pattern Recognition,
2007.CVPR'07. IEEE Conference on, pp. 1-8. IEEE, 2007.
AUTHORS
Currently a graduate student in Computational Science at University of Arkansas at
Little Rock, Victoria L. Fox is also a mathematics instructor for the University of
Arkansas at Monticello. Her professional interests include morphological image
processing, segmentation of natural images, and the incorporation of fuzzy logic in
multispectral image segmentation.
MariofonnaMilanova is a Professor of Computer Science Department at the University
of Arkansas at Little Rock since 2001.She received her M. Sc. degree in Expert
Systems and AI in 1991 and her Ph.D. degree in Computer Science in 1995 from the
Technical University, Sofia, Bulgaria. Dr.Milanova did her post-doctoral research in
visual perception at the University of Paderborn, Germany. She has extensive
academic experience at various academic and research organizations in different
countries. Milanova serves as a book editor of two books and associate editor of
several international journals. Her main research interests are in the areas of artificial intelligence,
biomedical signal processing and computational neuroscience, computer vision and communications,
machine learning, and privacy and security based on biometric research. She has published and co-
authored more than 70 publications, over 43 journal papers, 7 book chapters, numerous conference papers
and 2 patents.
Salim Al-Ali is a Ph.D. graduate student in the integrated computing of computer
science department at University of Arkansas at Little Rock (UALR). He received a
master degree from computer science department, Baghdad University, Iraq on 1995. He
is working as a teacher in Dohuk Technical Institute at Dohuk Polytechnic University.
His research interest field is computer vision in general, human action recognition,
image and video understanding.
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.4, August 2013
13
[21] Kamalakannan, S., S. Antani, R. Long, and G. Thoma. "Customized hybrid level sets for automatic
lung segmentation in chest x-ray images." In SPIE Medical Imaging, pp. 866939-866939.International
Society for Optics and Photonics, 2013.
[22] Ma, Liyan, and Jian Yu. "An unconstrained hybrid active contour model for image segmentation." In
Signal Processing (ICSP), 2010 IEEE 10th International Conference on, pp. 1098-1101. IEEE, 2010.
[23] Ali, Sahirzeeshan, and AnantMadabhushi. "Segmenting multiple overlapping objects via a hybrid
active contour model incorporating shape priors: applications to digital pathology."In SPIE Medical
Imaging, pp. 79622W-79622W.International Society for Optics and Photonics, 2011.
[24] Welk, Martin, Michael Breuß, and Oliver Vogel. "Morphological amoebas are self-snakes." Journal
of Mathematical Imaging and Vision 39.2 (2011): 87-99.
[25] Catté, Francine, Françoise Dibos, and Georges Koepfler. "A morphological scheme for mean
curvature motion and applications to anisotropic diffusion and motion of level sets."SIAM Journal on
Numerical Analysis 32.6 (1995): 1895-1909.
[26] Álvarez, Luis, Luis Baumela, Pedro Henríquez, and Pablo Márquez-Neila. "Morphological snakes."
In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 2197-2202.
IEEE, 2010.
[27] Marquez-Neila, Pablo, Baumela, Luis, Alvarez, Luis. “A Morphological Approach to Curvature-
based Evolution of Curves and Surfaces.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.106, June 2013.
[28] Kimmel, Ron. Numerical geometry of images: Theory, algorithms, and applications. Springer, 2004.
[29] T. Chan and L. Vese, “Active contours without edges,” IEEE Transactions on Image Processing,
10(2):266-277, 2001.
[30] Alpert, Sharon, MeiravGalun, Ronen Basri, and Achi Brandt. "Image segmentation by probabilistic
bottom-up aggregation and cue integration."In Computer Vision and Pattern Recognition,
2007.CVPR'07. IEEE Conference on, pp. 1-8. IEEE, 2007.
AUTHORS
Currently a graduate student in Computational Science at University of Arkansas at
Little Rock, Victoria L. Fox is also a mathematics instructor for the University of
Arkansas at Monticello. Her professional interests include morphological image
processing, segmentation of natural images, and the incorporation of fuzzy logic in
multispectral image segmentation.
MariofonnaMilanova is a Professor of Computer Science Department at the University
of Arkansas at Little Rock since 2001.She received her M. Sc. degree in Expert
Systems and AI in 1991 and her Ph.D. degree in Computer Science in 1995 from the
Technical University, Sofia, Bulgaria. Dr.Milanova did her post-doctoral research in
visual perception at the University of Paderborn, Germany. She has extensive
academic experience at various academic and research organizations in different
countries. Milanova serves as a book editor of two books and associate editor of
several international journals. Her main research interests are in the areas of artificial intelligence,
biomedical signal processing and computational neuroscience, computer vision and communications,
machine learning, and privacy and security based on biometric research. She has published and co-
authored more than 70 publications, over 43 journal papers, 7 book chapters, numerous conference papers
and 2 patents.
Salim Al-Ali is a Ph.D. graduate student in the integrated computing of computer
science department at University of Arkansas at Little Rock (UALR). He received a
master degree from computer science department, Baghdad University, Iraq on 1995. He
is working as a teacher in Dohuk Technical Institute at Dohuk Polytechnic University.
His research interest field is computer vision in general, human action recognition,
image and video understanding.