Performance evaluation of clustering algorithms on microcalcifications as mammography findings

Breast cancer can be prevented with regular mammography screening. Yet, the incorporation of Computational Intelligence relies on training classifiers on a set of predefined Regions of Interest (ROIs). Data Clustering has been applied to address the problem of ROI detection, yet no extensive research has been carried out on which algorithm to utilize. This contribution focuses on microcalcification clustering as a Data Clustering application, giving insights concerning the performance of three main clustering algorithms.

[1]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[2]  M Kallergi,et al.  Evaluating the performance of detection algorithms in digital mammography. , 1999, Medical physics.

[3]  M. N. Vrahatis,et al.  EFFICIENT UNSUPERVISED CLUSTERING THROUGH INTELLIGENT OPTIMIZATION , 2009 .

[4]  Claudio Marrocco,et al.  Algorithms for Detecting Clusters of Microcalcifications in Mammograms , 2005, ICIAP.

[5]  Hamid Soltanian-Zadeh,et al.  Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms , 2004, Pattern Recognit..

[6]  Dimitris K. Tasoulis,et al.  Improving the orthogonal range search k-windows algorithm , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[7]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[8]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[9]  L. Zhang,et al.  Advances in micro-calcification clusters detection in mammography , 2002, Comput. Biol. Medicine.

[10]  O. Nevalainen,et al.  Accurate segmentation of the breast region from digitized mammograms. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  Giuseppe Longo,et al.  Adaptive filtering in astronomical image processing. I: Basic considerations and examples , 1993 .

[12]  G Coppini,et al.  Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. , 2004, Medical engineering & physics.

[13]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[14]  Massimo Capaccioli,et al.  Adaptive filtering in astronomical image processing , 1991 .

[15]  A Bazzani,et al.  An SVM classifier to separate false signals from microcalcifications in digital mammograms , 2001, Physics in medicine and biology.

[16]  Robert M. Nishikawa,et al.  Current status and future directions of computer-aided diagnosis in mammography , 2007, Comput. Medical Imaging Graph..

[17]  Antonis Frigas,et al.  “Hippocrates-mst”: a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer , 2006, Medical and Biological Engineering and Computing.