A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

Jasmine,

doi:10.3844/ajeassp.2013.57.68

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…In [28] Abdall et.al used textural features with support vector machine and achieved 82.5% accuracy. Massotti [29] used SVM and achieved accuracy of 90%.J.S.Leena Jasmine et.al [30] used contourlet coefficients to extract the features, then the extracted features were given to SVM classifier the proposed method obtained an accuracy rate of 81%. Y.Ireaneus Anna Rejani [31] in their proposed method segmented the tumor region using thresholding technique and then morphological features are extracted from the segmented region.…”

Section: Support Vector Machinesmentioning

confidence: 98%

A study on mammography computer aided diagnosis system using machine learning methods

Dinakaran¹,

Sivakrithika²

2013

IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013)

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Early diagnosis is an important aspect of successful treatment for breast cancer. Mammogram is the most reliable imaging technique available. It is a challenging task for radiologists to detect the abnormalities in the mammograms. Computing helps the radiologists in diagnosing the abnormalities in the mammogram. Computer Aided Diagnosis System involves computerized biomedical image analysis to classify the mammography into benign or malign. In a decade of research work number of algorithms had been proposed to classify the image that employ data mining techniques, image processing methods, machine learning methods and pattern recognition. In this paper such algorithms in previous research work is studied and their performance is discussed.

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Section: Support Vector Machinesmentioning

confidence: 98%

A study on mammography computer aided diagnosis system using machine learning methods

Dinakaran¹,

Sivakrithika²

2013

IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013)

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“…These features were extracted from mass contours by new fractal features as described in Section "New Fractal Features in Diagnosis of Mass Types". Classification was done using SVM [6,13] on four data sets introduced in section "Data Sets". We used C-SVM classifier with linear kernel function and penalty term C equal to 1.…”

Section: Mass Classificationmentioning

confidence: 99%

An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms

et al. 2014

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In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, illdefined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis of different mass types, we defined new fractal features based on information extraction from the contours. This information is able to identify the roughness in mass contours and determines the extent of spiculation or smoothness of the masses. In this manner, in classification of the spiculated malignant masses from the circumscribed benign tumors, we achieved highly satisfactory results, i.e., 0.98 measured in terms of area under ROC curve (AUC). In this paper, it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification. The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors, we achieved high accuracy of 0.99 and 0.90 for AUC, respectively.

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“…For the purpose of classification, neural network is used. A system to detect the abnormalities in mammograms is presented in [7]. This work extracts features from non-subsampled contourlet.…”

Section: Introductionmentioning

confidence: 99%

Classification of Mammogram Abnormalities Using Pseudo Zernike Moments and SVM

Venkatalakshmi¹,

Janet²

2017

IJIGSP

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Abstract-The most common malignancy observed among Indian women is the breast cancer. However, the cancer is detectable earlier by means of mammograms. Computer Aided Diagnostic (CAD) techniques are the boon to medical industry and these techniques intend to support the physicians in diagnosis. In this paper, a novel CAD system for the detection and classification of the abnormalities in the mammogram is presented. The proposed work is organized into four important phases and they are pre-processing, segmentation, feature extraction and classification. The pre-processing phase intends to remove unwanted noise and make the mammograms suitable for the next process. The segmentation phase aims to extract the areas of interest to proceed with further process. Feature extraction is the most important phase, which is meant for extracting the texture features from the area of interest. This work employs pseudo zernike moments for extracting features, owing to the noise resistance power and description ability. Finally, Support Vector Machine (SVM) is employed as the classifier, so as to distinguish between the malignant and normal mammograms. The performance of the proposed work is evaluated by several experimentations and the results are satisfactory in terms of accuracy, specificity and sensitivity.

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A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

Cited by 13 publications

References 18 publications

A study on mammography computer aided diagnosis system using machine learning methods

A study on mammography computer aided diagnosis system using machine learning methods

An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms

Classification of Mammogram Abnormalities Using Pseudo Zernike Moments and SVM

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