Fully Automatic Computer-Aided Detection of Breast Cancer based on Genetic Algorithm Optimization

Breast cancer is the second disease causing death in women. The screening of breast cancer for early detection is an effective way to reduce mortality. Generally, mammography is the most effective imaging modality recommended by radiologists. Therefore, a high number of mammogram images have to be analyzed. Indeed, the expertise of radiologists and the quality of the images to be analyzed play a crucial role in the accuracy of the diagnosis. The use of CAD systems allow improving the accuracy of diagnosis by reducing the number of false-positive and the false-negative. In this paper, to assist radiologists to make the right decision by showing them the probably suspect area, a full automatic CADe is presented. The preprocessing step aims at delimiting the ROI by removing artifact and pectoral muscle using global thresholding method, morphological operators and seeded region growing method, followed by contrast enhancement of the ROI found in the first step using the technique named “adaptive local gray level transformation based on variable s-curve”. At last, the ROI is segmented using k-means clustering. Both contrast enhancement method and the segmentation method are performed by genetic algorithm to optimize the outcomes of each step.

[1]  L JayaV.,et al.  IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements , 2013 .

[2]  Mahrokh G. Shayesteh,et al.  Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing , 2013, Digit. Signal Process..

[3]  Yogita chauhan,et al.  A Survey Of K-Means And GA-KM The Hybrid Clustering Algorithm , 2014 .

[4]  P. Evans,et al.  Investigation of the accuracy of breast tissue segmentation methods for the purpose of developing breast deformation models for use in adaptive radiotherapy , 2014 .

[5]  Patricio A. Vela,et al.  A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..

[6]  Pai Jung Huang,et al.  Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms. , 2016, The British journal of radiology.

[7]  Mohammed Rafiq Abdul Kadir,et al.  Medical image contrast enhancement using spline concept: data from the osteoarthritis initiative , 2014 .

[8]  Rabins Porwal,et al.  Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images , 2016, Int. J. Biomed. Imaging.

[9]  Hamid El Malali,et al.  Adaptive Local Gray-Level Transformation Based on Variable S-Curve for Contrast Enhancement of Mammogram Images , 2020 .

[10]  Tomoe Barr,et al.  Automated breast segmentation of fat and water MR images using dynamic programming. , 2015, Academic radiology.

[11]  Wagner Coelho de Albuquerque Pereira,et al.  CAD and mammography : why use this tool ? , 2012 .

[12]  J. Dheeba,et al.  Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach , 2014, J. Biomed. Informatics.

[13]  Elizabeth Sherly,et al.  A Novel Approach for Removal of pectoral muscles in Digital Mammogram , 2015 .

[14]  R Nithya,et al.  Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer , 2011 .

[15]  Christine M. Anderson-Cook.  Practical Genetic Algorithms (2nd ed.) , 2005 .

[16]  Muhammad Sharif,et al.  Extraction of breast border and removal of pectoral muscle in wavelet domain , 2017 .

[17]  Wasim Arif,et al.  A Study of Different Texture Features Based on Local Operator for Benign-malignant Mass Classification☆ , 2016 .

[18]  Syamsiah Mashohor,et al.  Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection , 2017, EXCLI journal.

[19]  Yinan Kong,et al.  Involvement of Machine Learning for Breast Cancer Image Classification: A Survey , 2017, Comput. Math. Methods Medicine.

[20]  Jamal Zbitou,et al.  Statistical features and classification of normal and abnormal mammograms , 2014, 2014 International Conference on Multimedia Computing and Systems (ICMCS).

[21]  Yambem Jina Chanu,et al.  Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm , 2015 .

[22]  Wasfy B. Mikhael,et al.  Overview of Current Biomedical Image Segmentation Methods , 2016, 2016 International Conference on Computational Science and Computational Intelligence (CSCI).

[23]  Amjad Rehman,et al.  Medical Image Segmentation Methods, Algorithms, and Applications , 2014 .

[24]  Rangaraj M. Rangayyan,et al.  Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms , 2015, Medical & Biological Engineering & Computing.

[25]  Youssef Ben Youssef,et al.  Contour Detection of Mammogram Masses Using ChanVese Model and B-Spline Approximation , 2017, Int. J. Interact. Multim. Artif. Intell..

[26]  Sanjay N. Talbar,et al.  Local gray level S-curve transformation - A generalized contrast enhancement technique for medical images , 2017, Comput. Biol. Medicine.

[27]  Fathi E. Abd El-Samie,et al.  An algorithm for pre-processing and segmentation of mammogram images , 2016, 2016 11th International Conference on Computer Engineering & Systems (ICCES).