Analysis for the reconstruction of a noisy signal based on orthogonal moments
Applied Mathematics and Computation
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
On the computational aspects of Zernike moments
Image and Vision Computing
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Automatic Diagnosis of Masses by Using Level set Segmentation and Shape Description
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Classification of benign and malignant masses based on Zernike moments
Computers in Biology and Medicine
Automatic BI-RADS description of mammographic masses
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Mammogram retrieval on similar mass lesions
Computer Methods and Programs in Biomedicine
Image analysis by Tchebichef moments
IEEE Transactions on Image Processing
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
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Automatic classification of breast masses in mammograms has been considered a major challenge. Mass shape, margin and density define the malignancy level according to a standardized description, the BI-RADS lexicon. Unlike other approaches, we do not segment masses but instead, we attempt to describe entire regions. In this paper, continuos (Zernike) and discrete (Krawtchouk) orthogonal moments were used to characterize breast masses and their discriminant power to classify benign and malign masses, was assessed. Firstly, Regions of Interest selected by an expert are projected onto two sets of orthogonal polynomials functions, continuous and discrete, thereby drawing shape global information onto a feature space. Using a simple euclidean metric between vectors, the projected images are automatically classified as benign or malign by a k-nearest neighbor strategy. The parameter space is characterized using a set of 150 benign and 150 malign images. The whole method was assessed in a set of 100 masses with different shape and margins and the classification results were compared against a ground truth, already provided by the database. These results showed that discrete Krawtchouk outperformed Zernike moments, reaching an accuracy rate of 90,2% (compared to 81% for Zernike moments), while the area under the curve in a ROC evaluation yielded Az=0.93 and Az=0.85 for the Krawtchouk and Zernike strategies, respectively.