Benign and malignant breast cancer segmentation using optimized region growing technique

Abstract Breast cancer is one of the dreadful diseases that affect women globally. The occurrences of breast masses in the breast region are the main cause for women to develop a breast cancer. Early detection of breast mass will increase the survival rate of women and hence developing an automated system for detection of the breast masses will support radiologists for accurate diagnosis. In the pre-processing step, the images are pre-processed using Gaussian filtering. An automated detection method of breast masses is proposed using an optimized region growing technique where the initial seed points and thresholds are optimally generated using a swarm optimization technique called Dragon Fly Optimization (DFO). The texture features are extracted using GLCM and GLRLM techniques from the segmented images and fed into a Feed Forward Neural Network (FFNN) classifier trained using back propagation algorithm which classifies the images as benign and malignant. The performance of the proposed detection technique is evaluated using the images obtained from DDSM database. The results achieved by the proposed pixel-based technique are compared to other region growing methods using ROC analysis. The sensitivity of the proposed system reached up to 98.1% and specificity achieved is 97.8% in which 300 images are used for training and testing purposes.

[1]  Yudy Purnama,et al.  Mammogram Classification using Law's Texture Energy Measure and Neural Networks , 2015 .

[2]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[3]  Zhigang Zeng,et al.  A new automatic mass detection method for breast cancer with false positive reduction , 2015, Neurocomputing.

[4]  Brijesh Verma,et al.  A novel soft cluster neural network for the classification of suspicious areas in digital mammograms , 2009, Pattern Recognit..

[5]  Arturo J. Méndez,et al.  Computerized detection of breast masses in digitized mammograms , 2007, Comput. Biol. Medicine.

[6]  Yunsong Li,et al.  Breast mass classification in digital mammography based on extreme learning machine , 2016, Neurocomputing.

[7]  Ge Yu,et al.  Breast tumor detection in digital mammography based on extreme learning machine , 2014, Neurocomputing.

[8]  Arianna Mencattini,et al.  Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system , 2016, Knowl. Based Syst..

[9]  A. Vadivel,et al.  A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories , 2013, Comput. Biol. Medicine.

[10]  C. Metz Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[11]  A. Rényi On Measures of Entropy and Information , 1961 .

[12]  J. Hazel,et al.  BINARY (PRESENCE-ABSENCE) SIMILARITY COEFFICIENTS , 1969 .

[13]  Saroj Kumar Lenka,et al.  Texture-based features for classification of mammograms using decision tree , 2012, Neural Computing and Applications.

[14]  Qaisar Abbas,et al.  Breast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system , 2013, Biomed. Signal Process. Control..

[15]  Sasikala Jayaraman,et al.  Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images , 2016, J. King Saud Univ. Comput. Inf. Sci..

[16]  Manish Kumar Bajpai,et al.  Breast cancer detection in digital mammograms , 2015, 2015 IEEE International Conference on Imaging Systems and Techniques (IST).

[17]  Sanjay N. Talbar,et al.  Genetic Fuzzy System (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis☆ , 2016 .

[18]  Brijesh Verma,et al.  A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms , 2007, Appl. Soft Comput..

[19]  Wei Qian,et al.  An improved method of region grouping for microcalcification detection in digital mammograms. , 2002 .

[20]  J. Dheeba,et al.  A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms , 2012, Journal of Medical Systems.

[21]  Arnau Oliver,et al.  Topological Modeling and Classification of Mammographic Microcalcification Clusters , 2015, IEEE Transactions on Biomedical Engineering.

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

[24]  Shohreh Kasaei,et al.  Benign and malignant breast tumors classification based on region growing and CNN segmentation , 2015, Expert Syst. Appl..

[25]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[26]  Marcelo Zanchetta do Nascimento,et al.  Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm , 2014, Comput. Methods Programs Biomed..