scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Image segmentation using spatial intuitionistic fuzzy C means clustering

01 Dec 2014-pp 1-5
TL;DR: The algorithm proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM) that is comparatively less hampered by noise and performs better than existing algorithms.
Abstract: A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership functions within the neighborhood of the pixel under consideration. The results showed that this approach was not as sensitive to noise as compared to the traditional FCM algorithm and yielded better results. The algorithm we have proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM). Intuitionistic refers to the degree of hesitation that arises as a consequence of lack of information and knowledge. Proposed method is comparatively less hampered by noise and performs better than existing algorithms.
Citations
More filters
Journal ArticleDOI
Yangyang Li1, Cheng Peng1, Yanqiao Chen1, Licheng Jiao1, Linhao Zhou1, Ronghua Shang1 
TL;DR: The main idea of the method is to generate the classification results directly from the original two SAR images through a CNN without any preprocessing operations, which also eliminates the process of generating the difference image (DI), thus reducing the influence of the DI on the final classification result.
Abstract: With the rapid development of various technologies of satellite sensor, synthetic aperture radar (SAR) image has been an import source of data in the application of change detection. In this paper, a novel method based on a convolutional neural network (CNN) for SAR image change detection is proposed. The main idea of our method is to generate the classification results directly from the original two SAR images through a CNN without any preprocessing operations, which also eliminate the process of generating the difference image (DI), thus reducing the influence of the DI on the final classification result. In CNN, the spatial characteristics of the raw image can be extracted and captured by automatic learning and the results with stronger robustness can be obtained. The basic idea of the proposed method includes three steps: it first produces false labels through unsupervised spatial fuzzy clustering. Then we train the CNN through proper samples that are selected from the samples with false labels. Finally, the final detection results are obtained by the trained convolutional network. Although training the convolutional network is a supervised learning fashion, the whole process of the algorithm is an unsupervised process without priori knowledge. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of our algorithm in simulated and real data sets. In addition, we try to apply our algorithm to the change detection of heterogeneous images, which also achieves satisfactory results.

127 citations


Cites background from "Image segmentation using spatial in..."

  • ...In terms of the pixels in images, the adjacent elements, which have similar intensities and characteristic value, are highly likely to be assigned into the same cluster center [37]....

    [...]

Journal ArticleDOI
TL;DR: A new image segmentation method based on Dynamic Particle swarm optimization (DPSO) and FCM algorithm along with the noise reduction mechanism is proposed and the results show that the proposed algorithm has better performance and less sensitive to noise.
Abstract: Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness. However, FCM has the disadvantages of sensitivity to initial values, falling easily into local optimal solution and sensitivity to noise. To tackle these disadvantages, many optimization-based fuzzy clustering methods have been proposed in the literature survey. Particle swarm optimization (PSO) has good global optimization capability and a hybrid of FCM and PSO have improved accuracy over tradition FCM clustering. In this paper, a new image segmentation method based on Dynamic Particle swarm optimization (DPSO) and FCM algorithm along with the noise reduction mechanism is proposed. DPSO has the advantages to change the inertia weight and learning parameters dynamically. It adopts the inertia weight according to the fitness value and learning parameters along with time. The proposed method combines DPSO with FCM, using the advantages of global optimization searching and parallel computing of DPSO to find a superior result of the FCM algorithm. Moreover, a noise reduction mechanism based on the surrounding pixels is used for enhancing the anti-noise ability. The synthetic image and Magnetic Resonance Imaging (MRI) have been used for testing the proposed method by introducing different types of noises and the results show that the proposed algorithm has better performance and less sensitive to noise.

58 citations

Journal ArticleDOI
TL;DR: A Computer-Aided Diagnosis (CAD) framework for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been designed and implemented and produced superior results regarding accuracy, precision, recall, and specificity for the real-time dataset and the public dataset, respectively, when compared to the other bio-inspired algorithms.

20 citations

Proceedings ArticleDOI
08 Jul 2018
TL;DR: The Yager’s generating function is generalized in such a manner that the proposed IFS generation function tunes all the three components of the IFSs, and results obtained are better than the results obtained using YGF.
Abstract: We often have many datasets where hard clustering algorithms do not deliver satisfactory clustering results. It is found that many times fuzzy clustering technique improves the clustering results obtained by hard clustering algorithms. Fuzzy c-means (FCM) is the most prominent fuzzy clustering techniques whose improvement was proposed through the introduction of intuitionistic fuzzy set (IFS) based c-means algorithm. In order to implement IFS based c-means algorithm over a real valued dataset, data points were first converted into IFSs by employing a highly popular technique known as Yager’s generating function. The Yager’s generating function tunes only the non-membership and hesitancy component of an IFS. Therefore, IFS based c-means algorithm produces compromised clustering results. In this paper, we have generalized the Yager’s generating function in such a manner that our IFS generation function tunes all the three components of the IFSs. We have utilized the proposed IFS generation function in two highly used IFS based c-means algorithms of clustering known as intuitionistic fuzzy c-means (IFCM) and Novel intuitionistic fuzzy c-means (Novel-IFCM) algorithms on the UCI datasets. Our results obtained using the proposed function are better than the results obtained using YGF.

15 citations


Cites methods from "Image segmentation using spatial in..."

  • ...The applications of IFCM and Novel-IFCM were shown in some recently published papers (see [25], [26], [27] and[28], [29])....

    [...]

Journal ArticleDOI
TL;DR: An attempt has been made to segment the medical images using clustering method based on Intuitionistic fuzzy set called SIFCM, which is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information as well as capable of reduction of noisy spots and spurious blobs.
Abstract: Segmentation of images is one of the most challenging tasks because of restricted observation of the specialists and uncertainties presented in medical knowledge. Crisp values are inadequate to model real situation due to imprecise information frequently used in decision making process. Various intuitive methods have been explored to understand the ambiguity and uncertainty of medical images to carry out segmentation task. Therefore, in this paper, an attempt has been made to segment the medical images using clustering method based on Intuitionistic fuzzy set. With the incorporation of spatial information into intuitionistic clustering named as Spatial Intuitionistic Fuzzy C Means (SIFCM), the object of interest is segmented more accurately and effectively. The benefits of incorporating spatial information is that it is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information as well as capable of reduction of noisy spots and spurious blobs. The performances of proposed methods are evaluated for real images. The results indicate that SIFCM is more effective, and noise tolerant as compared with the fuzzy c-means clustering.

12 citations


Cites methods from "Image segmentation using spatial in..."

  • ...As few work has been based on the incorporation of spatial information into Intutionistic fuzzy set [11,12]....

    [...]

  • ...(c) Transformation of image into the intuitionistic domain by computing vA(x), μA(x)andπA(x) for the image f[11] (d) Center vector cj is computed by eq....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: Various properties are proved, which are connected to the operations and relations over sets, and with modal and topological operators, defined over the set of IFS's.

13,376 citations

Journal ArticleDOI
TL;DR: This paper presents a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering and yields regions more homogeneous than those of other methods.

1,296 citations


"Image segmentation using spatial in..." refers background or methods in this paper

  • ...Spatial FCM As described in [1], in case of an image, the correlation between the neighboring pixels is very high....

    [...]

  • ...sFCM [1], which is a two-step process, incorporates spatial information of the pixel in consideration....

    [...]

  • ...As explained in [1], increasing the parameter q which is the degree of the spatial function, modifies the membership function to accommodate spatial information to a greater degree, and produces better results....

    [...]

  • ...As given in [1], the partition coefficient Vpc [5] and partition entropy Vpe [6] are the representative functions for fuzzy partition....

    [...]

  • ...To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information....

    [...]

Journal ArticleDOI
TL;DR: This article evaluates the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, andA recently developed index I.
Abstract: In this article, we evaluate the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, and a recently developed index I. Based on a relation between the index I and the Dunn's index, a lower bound of the value of the former is theoretically estimated in order to get unique hard K-partition when the data set has distinct substructures. The effectiveness of the different validity indices and clustering methods in automatically evolving the appropriate number of clusters is demonstrated experimentally for both artificial and real-life data sets with the number of clusters varying from two to ten. Once the appropriate number of clusters is determined, the SA-based clustering technique is used for proper partitioning of the data into the said number of clusters.

1,247 citations


"Image segmentation using spatial in..." refers background in this paper

  • ...DB index [7] is known as the ratio of intra-cluster scatter to inter-cluster separation....

    [...]

  • ...The Dunn index [7] is the ratio between the minimal intercluster distance to maximal intra-cluster distance....

    [...]

Journal ArticleDOI
01 Jan 1973
TL;DR: This paper uses membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X.
Abstract: Given a finite, unlabelled set of real vectors X, one often presumes the existence of (c) subsets (clusters) in X, the members of which somehow bear more similarity to each other than to members of adjoining clusters. In this paper, we use membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X. Several numerical examples are offered in support of the proposed technique.

1,170 citations


"Image segmentation using spatial in..." refers background in this paper

  • ...They are defined as: 2N c ijj i pc u V N = (12) [ log ]N c ij ijj i pe u u V N − = (13) Maximizing Vpc or minimizing Vpe leads to better clustering....

    [...]

  • ...As given in [1], the partition coefficient Vpc [5] and partition entropy Vpe [6] are the representative functions for fuzzy partition....

    [...]

Journal ArticleDOI
TL;DR: This paper has reviewed, with somewhat variable coverage, the nine MR image segmentation techniques itemized in Table II; each has its merits and drawbacks.
Abstract: This paper has reviewed, with somewhat variable coverage, the nine MR image segmentation techniques itemized in Table II. A wide array of approaches have been discussed; each has its merits and drawbacks. We have also given pointers to other approaches not discussed in depth in this review. The methods reviewed fall roughly into four model groups: c-means, maximum likelihood, neural networks, and k-nearest neighbor rules. Both supervised and unsupervised schemes require human intervention to obtain clinically useful results in MR segmentation. Unsupervised techniques require somewhat less interaction on a per patient/image basis. Maximum likelihood techniques have had some success, but are very susceptible to the choice of training region, which may need to be chosen slice by slice for even one patient. Generally, techniques that must assume an underlying statistical distribution of the data (such as LML and UML) do not appear promising, since tissue regions of interest do not usually obey the distributional tendencies of probability density functions. The most promising supervised techniques reviewed seem to be FF/NN methods that allow hidden layers to be configured as examples are presented to the system. An example of a self-configuring network, FF/CC, was also discussed. The relatively simple k-nearest neighbor rule algorithms (hard and fuzzy) have also shown promise in the supervised category. Unsupervised techniques based upon fuzzy c-means clustering algorithms have also shown great promise in MR image segmentation. Several unsupervised connectionist techniques have recently been experimented with on MR images of the brain and have provided promising initial results. A pixel-intensity-based edge detection algorithm has recently been used to provide promising segmentations of the brain. This is also an unsupervised technique, older versions of which have been susceptible to oversegmenting the image because of the lack of clear boundaries between tissue types or finding uninteresting boundaries between slightly different types of the same tissue. To conclude, we offer some remarks about improving MR segmentation techniques. The better unsupervised techniques are too slow. Improving speed via parallelization and optimization will improve their competitiveness with, e.g., the k-nn rule, which is the fastest technique covered in this review. Another area for development is dynamic cluster validity. Unsupervised methods need better ways to specify and adjust c, the number of tissue classes found by the algorithm. Initialization is a third important area of research. Many of the schemes listed in Table II are sensitive to good initialization, both in terms of the parameters of the design, as well as operator selection of training data.(ABSTRACT TRUNCATED AT 400 WORDS)

1,036 citations