A new method based cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation

Abstract In these days, there are many various diseases, whose diagnosis is very hardly. Breast cancer is one of these type diseases. In this article, an diagnostic method is based on minimum-Redundancy-Maximal-Relevance m(RMR) and Kullback–Leibler (KL) Classifier for diagnosis of breast cancer. This diagnosis method is called as m(RMR)_KL. Minimum-Redundancy-Maximal-Relevance m(RMR) is used for feature selection. In this study the aim is to determine possibility of suspicious masses in mammogram. With this aim, probabilistic values of suspicious masses in the image are found via exponential curve fitting and texture features in order to find weight values in the objective function. Results are indicated on a scale to eliminate the suspicious lesions. Afterwards, images are classified as normal, malign, and benign by utilizing Kullback Leibler method. Here, 3 × 126 mammography images set selected from Digital Database for Screening Mammography (DDSM) are used, and severity of disease is probabilistically estimated. ROC analysis has been carried out to estimate the performance of the approach. Efficiency of the improved m(RMR)_KL method was tested as 98.3% accuracy diagnosis was obtained and it is very promising compared to the previously reported classification techniques. Thus, it is considered that workload of clinicians shall be reduced by easily eliminating suspicious images out of many mammography images.

[1]  Allan Kardec Barros,et al.  Diagnosis of Breast Cancer in Digital Mammograms Using Independent Component Analysis and Neural Networks , 2005, CIARP.

[2]  Abdulkadir Sengür,et al.  Wavelet packet neural networks for texture classification , 2007, Expert Syst. Appl..

[3]  Anselmo Cardoso de Paiva,et al.  Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley’s K Function , 2009, J. Signal Process. Syst..

[4]  Dejing Dou,et al.  Calculating Feature Weights in Naive Bayes with Kullback-Leibler Measure , 2011, 2011 IEEE 11th International Conference on Data Mining.

[5]  Arif Gülten,et al.  A Robust Multi-Class Feature Selection Strategy Based on Rotation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases , 2012, Journal of Medical Systems.

[6]  Asoke K. Nandi,et al.  Toward breast cancer diagnosis based on automated segmentation of masses in mammograms , 2009, Pattern Recognit..

[7]  Abdulkadir Sengur.  An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases , 2008 .

[8]  G. Braz,et al.  Breast Tissues Classification Based on the Application of Geostatistical Features and Wavelet Transform , 2007, 2007 6th International Special Topic Conference on Information Technology Applications in Biomedicine.

[9]  Anselmo Cardoso de Paiva,et al.  Classification of Normal, Benign and Malignant Tissues Using Co-occurrence Matrix and Bayesian Neural Network in Mammographic Images , 2006, 2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06).

[10]  Chris H. Q. Ding,et al.  Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..

[11]  Martin Kom,et al.  Automated detection of masses in mammograms by local adaptive thresholding , 2007, Comput. Biol. Medicine.

[12]  M. Masotti,et al.  A novel featureless approach to mass detection in digital mammograms based on support vector machines. , 2004, Physics in medicine and biology.

[13]  Haluk Eren,et al.  Cancer detection in mammograms estimating feature weights via Kullback-Leibler measure , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[14]  Abdulkadir Sengur.  Support Vector Machine Ensembles for Intelligent Diagnosis of Valvular Heart Disease , 2012, Journal of medical systems.

[15]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[16]  S Tangaro,et al.  A completely automated CAD system for mass detection in a large mammographic database. , 2006, Medical physics.

[17]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

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

[19]  Xosé R. Fernández-Vidal,et al.  Performance of the Kullback-Leibler information gain for predicting image fidelity , 2002, Object recognition supported by user interaction for service robots.

[20]  Abdulkadir Sengur.  Multiclass least-squares support vector machines for analog modulation classification , 2009 .

[21]  Debra M Ikeda,et al.  Computer-aided detection with screening mammography in a university hospital setting. , 2005, Radiology.

[22]  Georgia D. Tourassi,et al.  A Concentric Morphology Model for the Detection of Masses in Mammography , 2007, IEEE Transactions on Medical Imaging.

[23]  Berkman Sahiner,et al.  Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience. , 2005, Radiology.

[24]  P. Iosifidis,et al.  Population screening and intensity of screening are associated with reduced breast cancer mortality: evidence of efficacy of mammography screening in Australia , 2008, Breast Cancer Research and Treatment.

[25]  Mustafa Poyraz,et al.  A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE—JHT , 2014, Journal of Medical Systems.

[26]  J. Ferlay,et al.  Estimates of the cancer incidence and mortality in Europe in 2006. , 2006, Annals of oncology : official journal of the European Society for Medical Oncology.