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Journal ArticleDOI

Image Segmentation Using Rough Set Theory: A Review

01 Jul 2014-Vol. 1, Iss: 2, pp 62-74
TL;DR: In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly to provide a stable and better framework forimage segmentation.
Abstract: In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.
Citations
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Journal ArticleDOI
TL;DR: The basic concepts, operations and characteristics on the rough set theory are introduced, and then the extensions of rough set model, the situation of their applications, some application software and the key problems in applied research for the roughSet theory are presented.

185 citations

Journal ArticleDOI
TL;DR: In this paper, a novel chaotic bat algorithm (CBA) was proposed for multi-level thresholding in grayscale images using Otsu's between-class variance function.
Abstract: Multi-level thresholding is a helpful tool for several image segmentation applications Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA) Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321) Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives Therefore, it can be applied in complex image processing such as automatic target recognition

178 citations

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

102 citations


Cites methods from "Image Segmentation Using Rough Set ..."

  • ...Since, segmentation has a significant role in several applications [21-30], Thus, it is recommended to apply the proposed method in the medical domain applications as a future work....

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Journal ArticleDOI
TL;DR: A two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities is proposed and it is confirmed that AC offers enhanced results compared with other segmentation procedures considered in this article.

61 citations


Additional excerpts

  • ...(15) )) F T /( T ) F T /( T ( 2 / 1 CR B P N N N P P     (16) BCR 1 ER B   where IGT is GT, IS is the ROI, TN, TP, FN and FP are the related measures [51-54]....

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Journal ArticleDOI
TL;DR: Application of graphical user interface technique for the differentiation of acute lymphoblastic leukemia nucleus from healthy lymphocytes in a medical image is described, and experimental results depict that the above classification algorithms after optimizing with Jaya algorithm improve classification accuracy compared to the results obtained before optimizing withJaya algorithm.
Abstract: Early diagnosis of malignant leukemia can enormously help the physicians in choosing the right treatment for the patient A lot of diagnostic techniques are available to identify leukemia disease, but these techniques are costly Hence, there is a need for a less time-consuming and cost-effective method for the classification of leukemia blood cells In this paper, application of graphical user interface technique for the differentiation of acute lymphoblastic leukemia nucleus from healthy lymphocytes in a medical image is described This method employs backtrack search optimization algorithm for clustering Five different categories of features are extracted from the segmented nucleus images, ie, morphological, wavelet, color, texture and statistical features Feature selection plays a very important role in medical image processing It reduces the computational time and memory space The hybrid intelligent framework includes the benefits of the basic models; and in the meantime, it overcomes their limitations Three different kinds of hybrid supervised feature selection algorithms such as tolerance rough set particle swarm optimization-based quick reduct, tolerance rough set particle swarm optimization-based relative reduct and tolerance rough set firefly-based quick reduct are applied for selecting prominent features These algorithms incorporate the strengths of evolutionary algorithms The redundant features are eliminated to generate the reduced set which gives predictive capability equal to that of the original set of features Jaya algorithm is applied for optimizing the rules generated from classification algorithms Classification algorithms such as Naive Bayes, linear discriminant analysis, K-nearest neighbor, support vector machine, decision tree and ensemble random undersampling boost are applied on leukemia dataset Experimental results depict that the above classification algorithms after optimizing with Jaya algorithm improve classification accuracy compared to the results obtained before optimizing with Jaya algorithm

53 citations

References
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Journal ArticleDOI
TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

7,185 citations

Book
01 Jan 2005
TL;DR: This five-volume encyclopedia includes more than 550 articles highlighting current concepts, issues and emerging technologies that can be accessed by scholars, students, and researchers in the field of information science and technology.
Abstract: The Encyclopedia of Information Science and Technology is the first work to map this ever-changing field. It is the most comprehensive, research-based encyclopedia consisting of contributions from over 900 noted researchers in over 50 countries. This five-volume encyclopedia includes more than 550 articles highlighting current concepts, issues and emerging technologies. These articles are enhanced by special attention that is paid to over 4,500 technical and managerial terms. These terms will each have a 5-50 word description that allow the users of this extensive research source to learn the language and terminology of the field. In addition, these volumes offer a thorough reference section with over 11,500 sources of information that can be accessed by scholars, students, and researchers in the field of information science and technology.

907 citations

Journal ArticleDOI
TL;DR: A general segmentation method which can be applied to many different types of scenes and the potential performance of other segmentation techniques on general scenes is discussed.

540 citations


"Image Segmentation Using Rough Set ..." refers background in this paper

  • ...Rough set (Pawlak, 1982) refers to the formal approximation (Ohlander et al. 1978; Lindeberg & Li, 1997) of any conventional set....

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Book ChapterDOI
TL;DR: A new model to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets, which can detect objects whose boundaries are not necessarily defined by gradient is proposed.
Abstract: In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. We minimize an energy which can be seen as a particular case of the so-called minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable.

487 citations

Journal ArticleDOI
01 Nov 2009
TL;DR: A review of the current literature on rough- set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification and rough set frameworks hybridized with other computational intelligence technologies are presented.
Abstract: This paper presents a review of the current literature on rough-set- and near-set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction, and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm optimization, support vector machines, and fuzzy sets are also presented. In addition, a brief introduction to near sets and near images with an application to MRI images is given. Near sets offer a generalization of traditional rough set theory and a promising approach to solving the medical image correspondence problem as well as an approach to classifying perceptual objects by means of features in solving medical imaging problems. Other generalizations of rough sets such as neighborhood systems, shadowed sets, and tolerance spaces are also briefly considered in solving a variety of medical imaging problems. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

149 citations