Computer-Aided Diagnostic System based on Wavelet Analysis for Microcalcification Detection in Digital Mammograms

Alolfe, M.A.; Youssef, Abou-Bakr M.; Kadah, Yasser M.; Mohamed, A.S.A.

doi:10.1109/cibec.2008.4786080

Cited by 16 publications

(18 citation statements)

References 31 publications

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“…Computer based detection output can improve the diagnostic precision of detecting Microcalcifications since they might be easily unnoticed during routine due to their minute size. Morphological features such as shape, size, distribution pattern, density, and number of microcalcification are observed easily by distinguishing between malignant and benign microcalcification [7]. Malignant and benign microcalcification can occur with or without mass.…”

Section: A Microcalcification Featuresmentioning

confidence: 99%

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Microcalcification diagnosis in digital mammograms based on wavelet analysis and neural networks

Mina

Isa

2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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The classification of tumors is a medical application that poses a big challenge for in the field of breast cancer detection. The use of artificial intelligence and learning machine techniques has transformed the processes of diagnosis and analysis of breast cancer. For instance, the presence of clustered microcalcifications on X-ray mammograms is vital sign for early detection of breast cancer, which in turn prevents the dire option of surgical breast removal. Digital mammography offers several advantages such as the provision of digital information, which is available in a format that is usable by computer aided diagnosis systems. This work is based on two major approaches; wavelet decomposition analysis and neural network approaches. The system is classified normal from abnormal, mass from microcalcification. Experiments performed on the standard and publicly attainable dataset which is Mammography Image Analysis Society (MIAS), and a comparative analysis is carried out between the test results of this study and recent achievements reviewed in the literature..

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Section: A Microcalcification Featuresmentioning

confidence: 99%

“…This technique presupposes that image contains several homogeneous components that can be separated an appropriate option of intensity threshold [7]. After the application of image pruning, global gray level thresholding is subsequently applied.…”

Section: ) Global Gray Level Thresholdingmentioning

confidence: 99%

Microcalcification diagnosis in digital mammograms based on wavelet analysis and neural networks

Mina

Isa

2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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“…It also allows the given function to be analyzed at various levels of resolution [11] [12] [13]. DWT transforms a data vector into a numerically different vector of same length by a linear transformation in The information about the vertical, horizontal and diagonal sub-bands of an image is associated with the detail components and can be obtained by applying a high pass and low pass on an image respectively.…”

Section: B Feature Extractionmentioning

confidence: 99%

Mammogram mass classification based on discrete wavelet transform textural features

Jaleel¹,

Salim²,

Archana³

2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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This paper proposes an algorithm for early detection of breast cancer. This work incorporates Manual segmentation and textural analysis for the mammogram mass classification. Discrete Wavelet Transform (DWT) features act as a powerful input to the classifiers. A total of 148 mammogram images were taken from authentic mini MIAS database and under the supervision of classifiers, solid breast nodules were classified into benign and malignant. The classifiers used are K-Nearest Neighbor (K -NN), Support Vector Machine (SVM), Radial Basis Function Neural Network (R BFNN). It is found that RBFNN with DWT features outperform SVM and K-NN with 94.6% accuracy.The proposed system has a high potential for cancer detection from digital mammograms.

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“…Verma et al (7) developed a diagnosis algorithm based on a neural-genetic algorithm feature selection method for digital mammograms and the obtained accuracy was 85%. Alolfe et al (8) used the filter model and wrapper model for feature selection. Chen et al (9) proposed rough set-based feature selection.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data

2015

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Background:Breast cancer is one of the most encountered cancers in women. Detection and classification of the cancer into malignant or benign is one of the challenging fields of the pathology.Objectives:Our aim was to classify the mammogram data into normal and abnormal by ensemble classification method.Patients and Methods:In this method, we first extract texture features from cancerous and normal breasts, using the Gray-Level Co-occurrence Matrices (GLCM) method. To obtain better results, we select a region of breast with high probability of cancer occurrence before feature extraction. After features extraction, we use the maximum difference method to select the features that have predominant difference between normal and abnormal data sets. Six selected features served as the classifying tool for classification purpose by the proposed ensemble supervised algorithm. For classification, the data were first classified by three supervised classifiers, and then by simple voting policy, we finalized the classification process.Results:After classification with the ensemble supervised algorithm, the performance of the proposed method was evaluated by perfect test method, which gave the sensitivity and specificity of 96.66% and 97.50%, respectively.Conclusions:In this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. The obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy.

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Computer-Aided Diagnostic System based on Wavelet Analysis for Microcalcification Detection in Digital Mammograms

Cited by 16 publications

References 31 publications

Microcalcification diagnosis in digital mammograms based on wavelet analysis and neural networks

Microcalcification diagnosis in digital mammograms based on wavelet analysis and neural networks

Mammogram mass classification based on discrete wavelet transform textural features

Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data

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