New Particle Swarm Optimization for Feature Selection and Classification of Microcalcifications in Mammograms

An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications earlier and faster than typical screening programs. In this paper, Genetic Algorithm (GA) and New Particle Swarm Optimization (NPSO) algorithm are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with New Particle Swarm Optimization (BPN) for classification. And the Receiver Operating Characteristic (ROC) analysis is performed to evaluate the performance of the feature selection methods with their classification results. The proposed algorithms are tested with 114 abnormal images from the Mammography Image Analysis Society (MIAS) database.

[1]  Wayne Niblack,et al.  Feature selection with stochastic complexity , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Essam A. El-Kwae,et al.  Edge detection in medical images using a genetic algorithm , 1998, IEEE Transactions on Medical Imaging.

[3]  Mohamed Batouche,et al.  MRF-based image segmentation using Ant Colony System , 2003 .

[4]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[5]  Charles Kervrann,et al.  A Markov random field model-based approach to unsupervised texture segmentation using local and global spatial statistics , 1995, IEEE Trans. Image Process..

[6]  Atam P. Dhawan,et al.  Analysis of mammographic microcalcifications using gray-level image structure features , 1996, IEEE Trans. Medical Imaging.

[7]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[8]  Morton Nadler,et al.  Pattern recognition engineering , 1993 .

[9]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[10]  D. Kopans.  The positive predictive value of mammography. , 1992, AJR. American journal of roentgenology.

[11]  Cordelia Schmid,et al.  IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2004, Washington, DC, USA, June 27 - July 2, 2004 , 2004, CVPR Workshops.

[12]  Page Luck Zuckermann,et al.  Positive Predictive Value , 2009, Encyclopedia of Database Systems.

[13]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[14]  Zygmunt Pizlo,et al.  Preclinical ROC studies of digital stereomammography , 1995, IEEE Trans. Medical Imaging.

[15]  Anil K. Jain,et al.  Random field models in image analysis , 1989 .

[16]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[17]  N Karssemeijer,et al.  Automated classification of parenchymal patterns in mammograms. , 1998, Physics in medicine and biology.

[18]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[19]  D. E. Goldberg,et al.  Optimization and Machine Learning , 2022 .

[20]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[21]  Hamid Soltanian-Zadeh,et al.  Microcalcification classification in mammograms using multiwavelet features , 1999, Optics & Photonics.

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .