Feature Selection for the Classification of Both Individual and Clustered Microcalcifications in Digital Mammograms Using Genetic Algorithms

Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper uses a procedure for the classification of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG) and a Genetic Algorithm (GA) for feature selection. We found that the use of Genetic Algorithms (GAs) for selecting the features from microcalcifications and microcalcification clusters that will be the inputs of a feedforward Neural Network (NN) results mainly in improvements in overall accuracy, sensitivity and specificity of the classification.

[1]  Mandyam D. Srinath,et al.  Contour sequence moments for the classification of closed planar shapes , 1987, Pattern Recognit..

[2]  Joachim Dengler,et al.  Segmentation of Microcalcifications in Mammograms , 1991, DAGM-Symposium.

[3]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[4]  C. Boggis,et al.  Double reading of mammography screening films--one radiologist or two. , 1994, Clinical radiology.

[5]  E. Thurfjell,et al.  Benefit of independent double reading in a population-based mammography screening program. , 1994, Radiology.

[6]  Vasant Honavar,et al.  Evolutionary Design of Neural Architectures -- A Preliminary Taxonomy and Guide to Literature , 1995 .

[7]  Edward M. Ochoa.  Clustered Microcalcification Detection Using Optimized Difference of Gaussians. , 1996 .

[8]  Daphne Koller,et al.  Nonuniform Dynamic Discretization in Hybrid Networks , 1997, UAI.

[9]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[10]  Yianni Attikiouzel,et al.  Digitization Regime as a Cause for Variation in Algorithm Performance Across Two Mammogram Databases , 1999 .

[11]  Kathleen M. Harris,et al.  Analysis of False‐Negative Cancer Cases Identified with a Mammography Audit , 1999, The breast journal.

[12]  Thor Ole Gulsrud,et al.  Analysis of mammographic microcalcifications using a computationally efficient filter bank , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[13]  Hiroshi Fujita,et al.  Performance Evaluation of a CAD System for Detecting Masses on Mammograms by Using the MIAS Database , 2001 .

[14]  Chandrika Kamath,et al.  Evolving Neural Networks for the Classification of Galaxies , 2002, GECCO.

[15]  Byung-Woo Hong,et al.  Segmentation of mammograms in topographic approach , 2003 .

[16]  Hugo Terashima-Marín,et al.  Detection of Microcalcification Clusters in Mammograms Using a Difference of Optimized Gaussian Filters , 2005, ICIAR.

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