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Book ChapterDOI

Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C -Mean Clustering Algorithm

01 Jan 2018-pp 75-82
TL;DR: The proposed intuitionistic possibilistic fuzzy c-mean technique has been applied to the clustering of the mammogram images for breast cancer detector of abnormal images and results in high accuracy with clustering and breast cancer detection.
Abstract: There is a partitioning of a data set X into c-clusters in clustering analysis. In 1984, fuzzy c-mean clustering was proposed. Later, fuzzy c-mean was used for the segmentation of medical images. Many researchers work to improve the fuzzy c-mean models. In our paper, we proposed a novel intuitionistic possibilistic fuzzy c-mean algorithm. Possibilistic fuzzy c-mean and intuitionistic fuzzy c-mean are hybridized to overcome the problems of fuzzy c-mean. This proposed clustering approach holds the positive points of possibilistic fuzzy c-mean that will overcome the coincident cluster problem, reduces the noise and brings less sensitivity to an outlier. Another approach of intuitionistic fuzzy c-mean improves the basics of fuzzy c-mean by using intuitionistic fuzzy sets. Our proposed intuitionistic possibilistic fuzzy c-mean technique has been applied to the clustering of the mammogram images for breast cancer detector of abnormal images. The experiments result in high accuracy with clustering and breast cancer detection.
Citations
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Journal ArticleDOI
13 Jul 2020-Sensors
TL;DR: The proposed approach is highly effective with clustering and also with classification of breast cancer and has been compared with other available fuzzy clustering methods to prove the efficacy.
Abstract: The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

59 citations

Journal ArticleDOI
TL;DR: From results, it concludes that the proposed second algorithm is capable of estimate breast abnormal region boundary at high accuracy because it used fuzzy logic technique.
Abstract: Breast Cancer is one of the common and dangerous among women at the age of forty, so it is better for woman to have mammography testing as a significant step for the early detection of breast cancer and is diagnosis for treatment; There is an important need to an algorithm is used to determine the boundaries of the tumor in a finite accuracy. In this work, two algorithms were built depending on clustering approach as segmentation method. In the first algorithm has employed (K-mean) method, whilst in the second algorithm has employed fuzzy c-mean method (FCM). In both, the lazy snapping algorithm was used as an additional step to improve the segmentation performance of the detection of abnormal area. The proposed methods have been tested using mini-MIAS database, after assessment the results obtained. it indicates the accuracy of segmentation first algorithm, are 91.18% and accuracy of second algorithm is 94.12%. from results, it concludes that the proposed second algorithm is capable of estimate breast abnormal region boundary at high accuracy because it used fuzzy logic technique.

33 citations


Cites background from "Segmentation of Mammograms Using a ..."

  • ...Acharjya, [18] proposed a novel intuitionistic possibilistic Fuzzy C Means algorithm....

    [...]

Proceedings ArticleDOI
12 Aug 2016
TL;DR: This paper designates the digital execution of a model, based on an intuitionistic fuzzy histogram hyperbolization and possibilitic fuzzy c-mean clustering algorithm for early breast cancer detection, which was applied in classifier to detect about the presence of cancerous tumor in mammogram images.
Abstract: During past 20 years, it is stated that cancer belongings are mounting all-inclusive. Amid innumerable natures of cancers, breast cancer is witnessed as key reason of demise among women. Ultrasound, x-ray (mammograms and x-ray computed tomography), magnetic resonance imaging, thermography and nuclear medicine functional imaging are different modalities offered for early stage breast cancer detection. Mammography technology is a unadventurous breast cancer practice that can perceive tumorous masses on lower cost and better truthfulness. This paper designates the digital execution of a model, based on an intuitionistic fuzzy histogram hyperbolization and possibilitic fuzzy c-mean clustering algorithm for early breast cancer detection. Clustering plays a key role in segmentation fragment. Classical fuzzy clustering assigns data to multiple clusters at different degrees of membership but irrelevant data are also allocated to some clusters that do not relate to them. In our newfangled work we bound possibilistic method with fuzzy c-mean to resolve this issue after applying intuitionistic fuzzy histogram hyperbolization algorithm in initial preprocessing phase in the mammogram images. Further texture feature extraction technique is used for extracting features. Developed rules was applied in classifier to detect about the presence of cancerous tumor in mammogram images. The inclusive classification accuracy achieved 94% during training stage.

28 citations

Journal ArticleDOI
TL;DR: Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods and introduces sparse logistic regression for the early diagnosis of AD.
Abstract: Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment of AD. However, compared with AD classification tasks, predicting the conversion of MCI to AD is relatively difficult. as there are only minor differences among MCI groups. What’s more, in brain imaging analysis, the high dimensionality and relatively small number of subjects brings challenges to computer-aided diagnosis of AD and MCI. Many previous researches focused on the identification of imaging biomarkers for AD diagnosis. In this paper, we introduce sparse logistic regression for the early diagnosis of AD. Sparse logistic regression (SLR) uses L1/2 regularization to impose a sparsity constraint on logistic regression. The L1/2 regularization is considered a representative of Lq regularization, where fewer but informative key brain regions are applied for the classification of AD/MCI. We evaluated the SLR on 197 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that the SLR improves the classification performance of AD/MCI compared other classical methods.

26 citations

Proceedings ArticleDOI
01 Jan 2019
TL;DR: This paper provides the detailed review of various segmentation techniques proposed in the literature to extract microcalcification region of interest, masses, and breast lesions and to remove the pectoral muscles for mammogram images.
Abstract: In order to visualize the breast cancer radiologists prefer to use mammogram and breast ultrasound imaging modalities. To detect cancer, a region of interest (ROI) symbolizing tumor is extracted from the image. The segmentation process becomes tedious in presence of noise, low contrast, and blurriness. Pre-processing is done before segmentation to enhance the contrast and to remove the unwanted information from the image. Segmentation also influences the classification of the image into benign and malignant classes. Various segmentation techniques have been proposed in the literature to extract microcalcification region of interest, masses, and breast lesions and to remove the pectoral muscles. This paper provides the detailed review of these techniques, particularly for mammogram images.

21 citations


Cites background from "Segmentation of Mammograms Using a ..."

  • ...Chowdhary et al, 2018 [38] Intuitionistic possibilistic fuzzy c-mean clustering....

    [...]

References
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01 Jan 1967
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Abstract: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special

24,320 citations

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: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Abstract: The clustering problem is cast in the framework of possibility theory. The approach differs from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function. The advantages of the resulting family of possibilistic algorithms are illustrated by several examples. >

2,388 citations

Journal ArticleDOI
TL;DR: A new model called possibilistic-fuzzy c-means (PFCM) model, which solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM.
Abstract: In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicality values so that the sum over all data points of typicalities to a cluster is one. The row sum constraint produces unrealistic typicality values for large data sets. In this paper, we propose a new model called possibilistic-fuzzy c-means (PFCM) model. PFCM produces memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. PFCM is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM) that often avoids various problems of PCM, FCM and FPCM. PFCM solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM. We derive the first-order necessary conditions for extrema of the PFCM objective function, and use them as the basis for a standard alternating optimization approach to finding local minima of the PFCM objective functional. Several numerical examples are given that compare FCM and PCM to PFCM. Our examples show that PFCM compares favorably to both of the previous models. Since PFCM prototypes are less sensitive to outliers and can avoid coincident clusters, PFCM is a strong candidate for fuzzy rule-based system identification.

1,118 citations