Cancer detection in mammograms estimating feature weights via Kullback-Leibler measure

In this study the aim is to determine cancerous possibility of suspicious lesions in mammograms. With this aim, probabilistic values of suspicious lesions in the image are found via exponential curve fitting and texture features in order to find weight values in the objective function. Afterwards, images are classified as normal, malign, and benign by utilizing Kullback Leibler method. Here, 3×10 mammography images set selected from Digital Database for Screening Mammography (DDSM) are used, and severity of disease is probabilistically estimated. Results are indicated on a scale to eliminate the suspicious lesions. Thus, it is considered that workload of clinicians shall be reduced by easily eliminating suspicious images out of many mammography images.

[1]  Brijesh Verma,et al.  Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer , 2010, Expert Syst. Appl..

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

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

[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]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

[6]  Susan M. Astley,et al.  Model-based detection of spiculated lesions in mammograms , 1999, Medical Image Anal..

[7]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[8]  Christian Daul,et al.  Texture-based analysis of clustered microcalcifications detected on mammograms , 2012, Digit. Signal Process..

[9]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[10]  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.

[11]  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.

[12]  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.

[13]  Murk J. Bottema,et al.  Background intensity independent texture features for assessing breast cancer risk in screening mammograms , 2013, Pattern Recognit. Lett..