Classification of benign and malignant breast tumors based on hybrid level set segmentation

Rouhi, Rahimeh; Jafari, Mehdi

doi:10.1016/j.eswa.2015.10.011

Cited by 57 publications

(22 citation statements)

References 38 publications

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“…The order n is a non-negative integer, and the repetition m is an integer satisfying n -|m| = an even number and |m| ≤ n. j is the imaginary unit . Rn,m(ρ) is the 1-dimensional radial polynomial, which is defined as (15) As the Zernike moments are the projection of image f(x,y) onto these orthogonal basis functions, image I0 can be decomposed into a weighted sum of the Zernike polynomials (16) where An,m are the Zernike moments, which are the coefficients of the Zernike polynomials. The Zernike moments of image f(x,y) with continuous intensity are calculated according to the following equation (17) For a digital image of N×N pixels, the discrete form of the Zernike moments for an image is expressed as follows: (18) where λ=δA/π is a normalizing constant.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Breast mass segmentation is considered a crucial step in CAD systems. Several methods have proposed for segmentation of breast masses, such as the studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Terada et al [16] applied mean shift algorithm and an Iris filter to identify the possible regions and obtain gradient vectors of an image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Mean-shift Clustering and Statistical Analysis for Breast Mass Segmentation and Category

Wei¹,

Gwo²

2017

dtetr

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[Research objective] Breast mass segmentation is a crucial step in computer-aided diagnosis systems. The objective of this study is to propose a breast mass segmentation method using mean-shift clustering and statistical analysis. [Research method] The proposed method is described as follows. Firstly, the mean-shift algorithm splits the breast mass image into different clusters. Subsequently, the clusters of interest are identified by finding the overlapping of the split clusters and a circle covering the mass. Finally, Levene's test and Welch's test are both used to determine whether or not to merge clusters among clusters of interest. [Research result] The experimental results show that the proposed can perform mass segmentation effectively even if the margin of a mass is obscure and difficult to identify. In addition, the experiments also show that the Zernike moments method and the k-means method are effective methods to classify the shape of a breast mass.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Mean-shift Clustering and Statistical Analysis for Breast Mass Segmentation and Category

Wei¹,

Gwo²

2017

dtetr

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show abstract

“…The reported results show competitive achievements for ELM whoever inaccurate extraction of mass boundary will effect recognition performances. Besides SVM-based breast cancer diagnosis, there are a lot techniques have been developed such as Fuzzy cmean based [8], Genetic based [9], Linear Discriminate Analysis (LDA) based [10],Artificial Immune based [11],AdaBoost based [12],and level-set based [13].…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification Enhancement Based on Entropy Method

Sama¹,

Baneamoon²

2017

IJEACS

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Abstract-Breast cancer is the most common form of cancer diseases among women and the second leading cause of cancer deaths worldwide. However, due to some limitations in mammography, it is difficult to classify a suspicious mass in the breast as malignant (cancerous) or benign. This paper attempts to classify the mammographic masses with high accuracy by combining entropy method with evolutionary algorithm (EA) and fuzzy logic. EA and fuzzy logic are applied at training phase for parameters tuning, however, entropy method is applied at training phase and localization phase, where at training phase entropy enhances indicator of fuzziness, while at mass phase entropy enhances EA for the classification of masses in mammogram images. The proposed method is evaluated by experimenting a number of the benchmark Mini-MIAS databases and the results shows better and more effective identification.

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“…Each of these phases should be performed appropriately. In fact, the performance of each stage can affect that of the subsequent stages [4].…”

Section: Introductionmentioning

confidence: 99%

“…However, it evolves implicitly in level set. There are numerous studies on mass segmentation using level set methods [13]- [15]. Mass classification is a key technology in CADx systems.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Breast Mass Segmentation and Classification in Mammographic Images

Hmida¹,

Hamrouni²,

Solaiman³

et al. 2017

ijacsa

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Abstract-According to the World Health Organization, breast cancer is the main cause of cancer death among women in the world. Until now, there are no effective ways of preventing this disease. Thus, early screening and detection is the most effective method for rising treatment success rates and reducing death rates due to breast cancer. Mammography is still the most used as a diagnostic and screening tool for early breast cancer detection. In this work, we propose a method to segment and classify masses using the regions of interest of mammographic images. Mass segmentation is performed using a fuzzy active contour model obtained by combining Fuzzy C-Means and the Chan-Vese model. Shape and margin features are then extracted from the segmented masses and used to classify them as benign or malignant. The generated features are usually imprecise and reflect an uncertain representation. Thus, we propose to analyze them by a possibility theory to deal with imprecise and uncertain aspect. The experimental results on Regions Of Interest (ROIs) extracted from MIAS database indicate that the proposed method yields good mass segmentation and classification results.

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Classification of benign and malignant breast tumors based on hybrid level set segmentation

Cited by 57 publications

References 38 publications

Using Mean-shift Clustering and Statistical Analysis for Breast Mass Segmentation and Category

Using Mean-shift Clustering and Statistical Analysis for Breast Mass Segmentation and Category

Breast Cancer Classification Enhancement Based on Entropy Method

An Efficient Method for Breast Mass Segmentation and Classification in Mammographic Images

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