Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer

Beura, Shradhananda; Majhi, Banshidhar; Dash, Ratnakar

doi:10.1016/j.neucom.2014.12.032

Cited by 184 publications

(83 citation statements)

References 11 publications

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“…The results shows that the proposed system are accurate. A mammogram classification scheme have been proposed by [12]. The Gray level Co-Occurrence Matrix was utilized for the features.…”

Section: 1related Workmentioning

confidence: 99%

Speed up Robust Features (SURF) with Principal Component Analysis-Support Vector Machine (PCA-SVM) for benign and malignant classifications

Salleh

Mahmud

Yasiran

2018

J. Fundam and Appl Sci.

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“…The results shows that the proposed system are accurate. A mammogram classification scheme have been proposed by [12]. The Gray level Co-Occurrence Matrix was utilized for the features.…”

Section: 1related Workmentioning

confidence: 99%

Speed up Robust Features (SURF) with Principal Component Analysis-Support Vector Machine (PCA-SVM) for benign and malignant classifications

Salleh

Mahmud

Yasiran

2018

J. Fundam and Appl Sci.

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“…This was followed by the tumor area detection by using thresholding segmentation, and then by features extraction by using GMM. Kharrat, et al [8] and Beura, et al [9] used Grey Level Cooccurrence Matrix method (GLCM) and wavelet features to extract texture features in their studies. Kharrat, et al [8] developed an automated algorithm to classify the MRI brain tumors into normal, benign and malignant.…”

Section: Related Workmentioning

confidence: 99%

“…Kharrat, et al [8] developed an automated algorithm to classify the MRI brain tumors into normal, benign and malignant. Beura, et al [9] used these texture features to classify the breast tissues into normal, benign and malignant tumors by using mammogram images and the significance of the features were measured by using the F-statistic method. Pantelis [10] combined three methods for texture feature extraction; GLCM, first order statistical method and grey level run length matrix (GLRLM) to discriminate the normality and abnormality of MRI brain scanning images.…”

Section: Related Workmentioning

confidence: 99%

MRI brain scan classification using novel 3-D statistical features

Hasan

Meziane

Aspin

et al. 2017

Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing

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The paper presents an automated algorithm for detecting and classifying magnetic resonance brain slices into normal and abnormal based on a novel three-dimensional modified grey level co-occurrence matrix approach that is used for extracting texture features from MRI brain scans. This approach is used to analyze and measure asymmetry between the two brain hemispheres, based on the prior-knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten-fold cross-validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 93.3% using a Multi-Layer Perceptron Neural Network.

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“…These classification techniques can be divided into two categories. One is image analysis with segmentation the lesion areas [10][11][12][13][14][15][16], and the other is image analysis without segmentation [17][18][19][20][21][22][23]. Wei et al [10] come up with a content-based mammogram retrieval system; meanwhile, a similarity measure scheme was proposed, this study was tested on Digital Database for Screening Mammography (DDSM) dataset, and experimental results demonstrated that round-shape masses were most discriminative when using Zernike moments and roundshape, circumscribed margin masses could achieve the highest precision among all mass types.…”

Section: Introductionmentioning

confidence: 99%

“…Pak et al [22] used ROI-feature extraction based on Nonsubsampled Contourlet Transform (NSCT) and Super Resolution (SR); then AdaBoost algorithm was used to classify and determine the probability of benign and malignant. Beura et al [23] employed Gray Level Cooccurrence Matrix (GLCM) to all the detailed wavelet coefficients based on ROI and then classified the breast tissues as normal, benign, or malignant using Back Propagation Neural Network (BPNN).…”

Section: Introductionmentioning

confidence: 99%

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

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Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

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Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer

Cited by 184 publications

References 11 publications

Speed up Robust Features (SURF) with Principal Component Analysis-Support Vector Machine (PCA-SVM) for benign and malignant classifications

Speed up Robust Features (SURF) with Principal Component Analysis-Support Vector Machine (PCA-SVM) for benign and malignant classifications

MRI brain scan classification using novel 3-D statistical features

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

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