AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
On the computational aspects of Zernike moments
Image and Vision Computing
A new class of Zernike moments for computer vision applications
Information Sciences: an International Journal
Computers in Biology and Medicine
Evaluation of shape similarity measurement methods for spine X-ray images
Journal of Visual Communication and Image Representation
Breast mass classification using orthogonal moments
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
Uncertainty modeling for ontology-based mammography annotation with intelligent BI-RADS scoring
Computers in Biology and Medicine
Breast mass contour segmentation algorithm in digital mammograms
Computer Methods and Programs in Biomedicine
MIAPS: A web-based system for remotely accessing and presenting medical images
Computer Methods and Programs in Biomedicine
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Enormous numbers of digital mammograms have been produced in hospitals and breast screening centers. To exploit those valuable resources in aiding diagnoses and research, content-based mammogram retrieval systems are required to effectively access the mammogram databases. This paper presents a content-based mammogram retrieval system, which allows medical professionals to seek mass lesions that are pathologically similar to a given example. In this retrieval system, shape and margin features of mass lesions are extracted to represent the characteristics of mammographic lesions. To compare the similarity between the query example and any lesion within the databases, this study proposes a similarity measure scheme which involves the hierarchical arrangement of mammographic features and a weighting distance measure. This makes similarity measure of the retrieval system consistent with the way radiologists observe mass lesions. This study used the DDSM dataset to evaluate the effectiveness of the extracted shape feature and margin feature, respectively. Experimental results demonstrate that, when Zernike moments are used, round-shape masses are the most discriminative among four types of shape; the circumscribed-margin masses can be effectively discriminated among the four types of margins. Moreover, the result also shows that, when retrieving round-shape and circumscribed margin masses, this retrieval system can achieve the highest precision among all mass lesion types.