Joaquim C. Felipe
Caetano Jr Traina
ABSTRACT
This paper presents a new approach to support Computer-aided Diagnosis (CAD) aiming at assisting the task of classification and similarity retrieval of mammographic mass lesions, based on shape content. We have tested classical algorithms for automatic segmentation of this kind of image, but usually they are not precise enough to generate accurate contours to allow lesion classification based on shape analyses. Thus, in this work, we have used Zernike moments for invariant pattern recognition within regions of interest (ROIs), without previous segmentation of images. A new data mining algorithm that generates statistical-based association rules is used to identify representative features that discriminate the disease classes of images. In order to minimize the computational effort, an algorithm based on fractal theory is applied to reduce the dimension of feature vectors. K-nearest neighbor retrieval was applied to a database containing images excerpted from previously classified digitalized mammograms presenting breast lesions. The results reveal that our approach allows fast and effective feature extraction and is robust and suitable for analyzing this kind of image.
- Antani, S. K., et al., Developing a comprehensive system for content-based image retrieval of image and text from a national survey. SPIE Medical Imaging, 2005, 5748: p. 152--161.]]Google Scholar
- Aumann, Y. and Y. Lindell. A Statistical Theory for Quantitative Association Rules. in Fifth SIGKDD Int. Conf. on Knowledge Discovery and Data Mining. 1999. San Diego, California.]] Google ScholarDigital Library
- Balan, A. G. R., et al. Fractal Analysis of Image Textures for Indexing and Retrieval by Content. in 18th IEEE Symposium on Computer-Based Medical Systems (CBMS). 2005. Dublin, Ireland.]] Google ScholarDigital Library
- Beucher, S. The watershed transformation applied to image segmentation. in 10th Pfefferkorn Conf. on Signal and Image Processing in Microscopy and Microanalysis. 1991. Cambridge, UK.]]Google Scholar
- Beyer, K., et al. When is "Nearest Neighbor" Meaningful? in Int. Conf. on Database Theory (ICDT). 1999. Jerusalem, Israel.]] Google ScholarDigital Library
- Bueno, J. M., et al. How to Add Content-based Image Retrieval Capability in a PACS. in IEEE Int. Conf. on Computer Based Medical Systems - CBMS. 2002. Maribor, Slovenia.]] Google ScholarDigital Library
- Comer, M. L. and E. J. Delp, The EM/MPM Algorithm for Segmentation of Textured Images: Analysis and Further Experimental Results. IEEE Trans. Image Processing, 2000. 9(10): p. 1731--1744.]]Google ScholarCross Ref
- Khotanzad, A. and Y. H. Hong, Invariant Image Recognition by Zernike Moments. Transactions on Pattern Analysis and Machine Inteligence, 1990. 12(5): p. 489 -- 497.]] Google ScholarDigital Library
- Lehmann, T. M., et al. Content-based Image Retrieval in Medical Applications for Picture Archiving and Communication Systems. in SPIE 2003. 2003.]]Google Scholar
- Lehmann, T. M., B. B. Wein, and H. Greenspan. Integration of Content-based Image Retrieval to Picture Archiving and Communication Systems. in Medical Informatics Europe (MIE 2003). 2003. St Malo, France.]]Google Scholar
- Mlsna, P. A. and N. M. Sirakov. Intelligent Shape Feature Extraction and Indexing for Efficient Content-Based Medical Image Retrieval. in IEEE Southwest Symposium on Image Analysis and Interpretation. 2004. Lake Tahoe, Nevada, USA.]]Google Scholar
- Müller, H., et al., A review of content-based image retrieval systems in medical applications - clinical menefits and future directions. International Journal of Medical Informatics, 2004. 73: p. 1 -- 23.]]Google ScholarCross Ref
- Quinlan, J. R., Induction of Decision Trees, in Machine Learning Magazine. 1986. p. 81--106.]] Google ScholarDigital Library
- Ribeiro, M. X., et al. Mining Statistical Association Rules to Select the Most Relevant Medical Image Features. in The First Int. Workshop on Mining Complex Data (in conjunction with The Fifth IEEE International Conference on Data Mining). 2005. Houston, Texas, USA.]]Google Scholar
- Sousa, E. P. M., et al. How to Use Fractal Dimension to Find Correlations between Attributes. in First Workshop on Fractals and Self-similarity in Data Mining (in conjunction with 8th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining). 2002. Alberta, Canada.]]Google Scholar
- Zhang, D. S. and G. Lu. Content-Based Shape Retrieval Using Different Shape Descriptors: A Comparative Study. in IEEE Int. Conf. on Multimedia and Expo. 2001. Tokyo. Japan.]]Google Scholar
Index Terms
- Effective shape-based retrieval and classification of mammograms
Recommendations
Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric
Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR ...
Inter- and intra-observer variability of radiologists evaluating CBIR systems
IWDM'12: Proceedings of the 11th international conference on Breast ImagingThe purpose of the study is to evaluate the inter- and intra-observer variability of the radiologists in evaluation of the similarity between the query and retrieved ulatasound images containing breast masses by the Content-Based Image Retrieval (CBIR) ...
Classification of benign and malignant breast cancer based on DWI texture features
ICBCI 2017: Proceedings of the International Conference on Bioinformatics and Computational IntelligenceIn this paper, the purpose was to develop a computer-aided diagnosis (CADs) system that use diffusion-weighted Magnetic resonance image (DW-MRI) for distinguishing benign and malignant breast tumors. Sixty-one cases were collected, including 23 patients ...
Comments