On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Inside
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Approaches for automated detection and classification of masses in mammograms
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A novel approach to the fast computation of Zernike moments
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A Hybrid Algorithm of Fast and Accurate Computing Zernike Moments
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Development of tolerant features for characterization of masses in mammograms
Computers in Biology and Medicine
Complex Zernike moments features for shape-based image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
Expert Systems with Applications: An International Journal
Feature Extraction from Contours Shape for Tumor Analyzing in Mammographic Images
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
A zernike moment phase-based descriptor for local image representation and matching
IEEE Transactions on Image Processing
A new edge detection method using Gaussian-Zemike moment operator
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Breast mass classification using orthogonal moments
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Fast opposite weight learning rules with application in breast cancer diagnosis
Computers in Biology and Medicine
Computers in Biology and Medicine
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In mammography diagnosis systems, high False Negative Rate (FNR) has always been a significant problem since a false negative answer may lead to a patient's death. This paper is directed towards the development of a novel Computer-aided Diagnosis (CADx) system for the diagnosis of breast masses. It aims at intensifying the performance of CADx algorithms as well as reducing the FNR by utilizing Zernike moments as descriptors of shape and margin characteristics. The input Regions of Interest (ROIs) are segmented manually and further subjected to a number of preprocessing stages. The outcomes of preprocessing stage are two processed images containing co-scaled translated masses. Besides, one of these images represents the shape characteristics of the mass, while the other describes the margin characteristics. Two groups of Zernike moments have been extracted from the preprocessed images and applied to the feature selection stage. Each group includes 32 moments with different orders and iterations. Considering the performance of the overall CADx system, the most effective moments have been chosen and applied to a Multi-layer Perceptron (MLP) classifier, employing both generic Back Propagation (BP) and Opposition-based Learning (OBL) algorithms. The Receiver Operational Characteristics (ROC) curve and the performance of resulting CADx systems are analyzed for each group of features. The designed systems yield Az=0.976, representing fair sensitivity, and Az=0.975 demonstrating fair specificity. The best achieved FNR and FPR are 0.0% and 5.5%, respectively.