The International Arab Journal of Information Technology (IAJIT)

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Segmentation of Mammogram Abnormalities Using Ant System based Contour Clustering Algorithm

Breast cancer is the most widespread cancer that affects females all over the world. The Computer-aided Detection Systems (CADs) could assist radiologists’ in locating and classifying the breast tissues into normal and abnormal, however the absolute decisions are still made by the radiologist. In general, CAD system consists of four stages: Pre-processing, segmentation, feature extraction, and classification. This research work focuses on the segmentation step, where the abnormal tissues are segmented from the normal tissues. There are numerous approaches presented in the literature for mammogram segmentation. The major limitation of these methods is that they have to test each and every pixel of the image at least once, which is computationally expensive. This research work focuses on detection of microcalcifications from the digital mammograms using a novel segmentation approach based on novel Ant Clustering approach called Ant System based Contour Clustering (ASCC) that simulates the ants’ foraging behavior. The performance of the ASCC based segmentation algorithm is investigated with the mammogram images received from Mammographic Image Analysis Society (MIAS) database.

  1. Abbas Q., Fondo'n I., and Celebi E., “A Computerized System for Detection of Spiculated Margins Based on Mammography,” The International Arab Journal of Information Technology, vol. 12, no. 6, pp. 582-587, 2015.
  2. Abdullah H. and Jasim A., “Improved Ant Colony Optimization for Document Image Segmentation,” International Journal of Computer Science and Information Security, vol. 14, no. 11, pp. 775-785, 2016.
  3. Agrawal P., Vatsa M., and Singh R., “Saliency Based Mass Detection from Screening Mammograms,” Signal Processing, vol. 99, pp. 29-47, 2014.
  4. Angayarkanni S., Kamal N., and Thangaiya R., “Dynamic Graph Cut Based Segmentation of Mammogram,” SpringerPlus, vol. 4, no. 1, pp. 1-9, 2015.
  5. Cascio D., Fauci F., Magro R., Raso G., Bellotti R., De Carlo F., Tangaro S., De Nunzio G., Quarta M., Forni G., and Lauria A., “Mammogram Segmentation by Contour Searching and Mass Lesions Classification with Neural Network,” IEEE Transactions on Nuclear Science, vol. 53, no. 5, pp. 2827-2833, 2006.
  6. Chalana V. and Kim Y., “A Methodology for Evaluation of Boundary Detection Algorithms on Medical Images,” IEEE Transactions on Medical Imaging, vol. 16, no. 5, pp. 642-652, 1997.
  7. Chowdhary C. and Acharjya D., “Segmentation of Mammograms Using a Novel Intuitionistic Possibilistic Fuzzy C-Mean Clustering Algorithm,” in Proceedings of the Nature Inspired Computing, Singapore, pp. 75-82, 2018.
  8. De Nazaré Silva J., De Carvalho Filho A., Silva A., De Paiva A., and Gattass M., “Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM,” Journal of Digital Imaging, vol. 28, no. 3, pp. 323-337, 2015.
  9. Deneubourg J., Goss S., Franks N., Sendova-Franks A., Detrain C., and Chrétien L., “The Dynamics of Collective Sorting Robot-like Ants and Ant-like Robots,” in Proceedings of the 1st International Conference on Simulation of Adaptive Behavior on from Animals to Animats, Cambridge, pp. 356-365, 1991.
  10. Dixon A., “Diagnostic Breast Imaging: Mammography, Sonography, Magnetic Resonance Imaging, and Interventional Procedures,” Ultrasound: Journal of the British Medical Ultrasound Society, vol. 22, no. 3, pp. 182-183, 2014.
  11. Dorigo M., Maniezzo V., and Colorni A., “Ant System: Optimization by A Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, no. 1, pp. 29-41, 1996.
  12. Duarte M., Alvarenga A., Azevedo C., Calas M., Infantosi A., and Pereira W., “Evaluating Geodesic Active Contours in Microcalcifications Segmentation on Mammograms,” Computer Methods and Programs in Biomedicine, vol. 122, no. 3, pp. 304-315, 2015.
  13. Feng Y., “Ant Colony Cooperative Optimization and its Application in Image Segmentation,” Ph. D. Theses, Xi’an Jiaotong University, 2005.
  14. Ghosh S., Kothari M., Halder A., and Ghosh A., “Use of Aggregation Pheromone Density for Image Segmentation,” Pattern Recognition Letters, vol. 30, no. 10, pp. 939-949, 2009.
  15. Han Y. and Shi P., “An Improved Ant Colony Algorithm for Fuzzy Clustering in Image Segmentation,” Neurocomputing, vol. 70, no. 4-6, pp. 665-671, 2007.
  16. Hao Y., Yang H., Long B., and Liu J., “Image Segmentation Based on a New Self-Adaptive Ant Clustering Algorithm,” in Proceedings of the IEEE International Conference on Apperceiving Computing and Intelligence Analysis Proceeding, Chengdu, pp. 258-261, 2010.
  17. Hung C. and Sun M., “Ant Colony Optimization for the K-Means Algorithm in Image Segmentation,” in Proceedings of the 48th Annual Southeast Regional Conference, Mississippi, pp. 1-4, 2010.
  18. İnkaya T., Kayalıgil S., and Özdemirel N., “Ant Colony Optimization Based Clustering Methodology,” Applied Soft Computing, vol. 28, pp. 301-311, 2015.
  19. Jaccard P., “Nouvelles Recherches Sur La Distribution Florale,” Bulletin Société Vaudoise des Sciences Naturelles, vol. 44, no. 163, pp. 223-270, 1908.
  20. Jevtić A., Quintanilla-Domínguez J., Barrón-Adame J., and Andina D., “Image Segmentation Using Ant System-Based Clustering Algorithm,” in Proceedings of the 6th International Conference on Soft Computing Models in Industrial and Environmental Applications, Berlin, pp. 35-45, 2011.
  21. Khorram B. and Yazdi M., “A New Optimized Thresholding Method Using Ant Colony Algorithm for MR Brain Image Segmentation,” Journal of Digital Imaging, vol. 32, no. 1, pp. 162-174, 2019.
  22. Laptik R. and Navakauskas D., “Application of Ant Colony Optimization for Image Segmentation,” Elektronika Ir Elektrotechnika, vol. 80, no. 8, pp. 13-18, 2007.
  23. Li L., Ren Y., and Gong X., “Medical Image Segmentation Based on Modified Ant Colony Algorithm with GVF Snake Model,” in Proceedings of the IEEE International Seminar on Future BioMedical Information Engineering, Wuhan, pp. 11-14, 2008.
  24. Liu L., Tan G., and Soliman M., “Color Image Segmentation Using Mean Shift and Improved Ant Clustering,” Journal of Central South University, vol. 19, no. 4, pp. 1040-1048, 2012.
  25. Lumer E. and Faieta B., “Diversity and Adaptation in Populations of Clustering Ants,” in Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, Cambridge, pp. 501-508, 1994.
  26. Moftah H., Azar A., Al-Shammari E., Ghali N., Hassanien A., and Shoman M., “Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation,” Neural Computing and Applications, vol. 24, no. 7, pp. 1917-1928, 2014.
  27. Odet C., Belaroussi B., and Benoit-Cattin H., “Scalable Discrepancy Measures for Segmentation Evaluation,” in Proceedings of the IEEE International Conference on Image Processing, Rochester, pp. I-I, 2002.
  28. Oliver A., Freixenet J., Marti J., Perez E., Pont J., Denton E., and Zwiggelaar R., “A Review of Automatic Mass Detection and Segmentation in Mammographic Images,” Medical Image Analysis, vol. 14, no. 2, pp. 87-110, 2010.
  29. Ouadfel S. and Batouche M., “An Efficient Ant Algorithm for Swarm-Based Image Clustering,” Journal of Computer Science, vol. 3, no. 3, pp. 162-167, 2007.
  30. Ouadfel S. and Batouche M., “MRF-based Image Segmentation Using Ant Colony System,” ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol. 2, no. 1, pp. 12-24, 2003.
  31. Ouadfel S., Batouche M., and Garbay C., “Ant Colony System for Image Segmentation Using Markov Random Field,” in Proceedings of the International Workshop on Ant Algorithms, Berlin, pp. 294-295, 2002.
  32. Rosa B., Mozer P., and Szewczyk J., “An

Algorithm for Calculi Segmentation on Ureteroscopic Images,” International Journal of Computer Assisted Radiology and Surgery, vol. 6, no. 2, pp. 237-246, 2011.

  1. Rouhi R. and Jafari M., “Classification of Benign and Malignant Breast Tumors Based on Hybrid Level Set Segmentation,” Expert Systems with Applications, vol. 46, pp. 45-59, 2016.
  2. Saatchi S. and Hung C., “Using Ant Colony Optimization and Self-Organizing Map for Image Segmentation,” in Proceedings of the Mexican International Conference on Artificial Intelligence, Berlin, pp. 570-579, 2007.
  3. Sampaio W., Diniz E., Silva A., De Paiva A., and Gattass M., “Detection of Masses in Mammogram Images Using CNN, Geostatistic Functions and SVM,” Computers in Biology and Medicine, vol. 41, no. 8, pp. 653-664, 2011.
  4. Sezgin M. and Sankur B., “Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-165, 2004.
  5. Wang X., Feng Y., and Feng Z., “Ant Colony Optimization for Image Segmentation,” in Proceedings of the IEEE International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 5355-5360, 2005.
  6. Yan J., “Remote Sensing Image Segmentation Based on Ant Colony Optimized Fuzzy C-Means Clustering,” Journal of Chemical and Pharmaceutical Research, vol. 6, no. 6, pp. 2675-2679, 2014.
  7. Yang X., Zhao W., Chen Y., and Fang X., “Image Segmentation with a Fuzzy Clustering Algorithm Based on Ant-Tree,” Signal Processing, vol. 88, no. 10, pp. 2453-2462, 2008.
  8. Yasnoff W., Mui J., and Bacus J., “Error Measures for Scene Segmentation,” Pattern Recognition, vol. 9, no. 4, pp. 217-231, 1977.
  9. Yu J., Lee S., and Jeon M., “Medical Image Segmentation by Hybridizing Ant Colony Optimization and Fuzzy Clustering Algorithm,” in Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, Dublin, pp. 217-218, 2011.
  10. Yuanjing F., Li Y., and Liangjun K., “Finite Grade Pheromone Ant Colony Optimization for Image Segmentation,” Opto-Electronics Review, vol. 16, no. 2, pp. 163-171, 2008.
  11. Zhang Y., “A Survey on Evaluation Methods for Image Segmentation,” Pattern Recognition, vol. 29, no. 8, pp. 1335-1346, 1996.
  12. Zhao B., Zhu Z., Mao E., and Song Z., “Image Segmentation Based on Ant Colony Optimization and K-Means Clustering,” in Proceedings of the IEEE International Conference on Automation and Logistics, Jinan, pp. 459-463, 2007.
  13. Zou G., “Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain Images,” International Journal Bioautomation, vol. 20, no. 4, pp. 505-514, 2016.
  14. Zou R., Yu W., Yu Z., and Yu X., “Image Segmentation Based on Local Ant Colony Optimization,” in Proceedings of the IEEE 5th International Conference on Natural Computation, Tianjian, pp. 35-39, 2009.