Interactive Medical Image Segmentation using Active Contour with Improved F Energy in Level-Set Tuning

سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 296

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شناسه ملی سند علمی:

JR_MJEE-16-2_002

تاریخ نمایه سازی: 3 آذر 1401

چکیده مقاله:

Segmentation is a fundamental element in medical image processing (MIP) and has been extensively researched and developed to aid in clinical interpretation and utilization. This article discusses a method for segmenting abnormal masses or tumors in medical images that is both robust and effective. We suggested a method based on Active Contour (AC) and modified Level-set techniques to detect malignancies in magnetic resonance imaging (MRI), mammography, and computed tomography (CT). To segment malignant masses, the active contour approach, the energy function, the level-set method, and the proposed F function are employed. The system was evaluated using ۱۶۰ medical images from two databases, including ۸۰ mammograms and ۸۰ MRI brain scans. The algorithm for segmenting suspicious segments has an accuracy, recall, and precision of ۹۶.۲۵%, ۹۵.۶۰%, and ۹۵.۷۱%, respectively. By adding this technique into tissue imaging devices, the accuracy of diagnosing images with a relatively large volume that are evaluated fast is increased. Cost savings, time savings, and high precision are all advantages of the approach that set it apart from similar systems.

نویسندگان

Khosro Rezaee

Department of Biomedical Engineering, Meybod University, Meybod, Iran.

Mohammad Khalil Nakhl Ahmadi

Islamic Azad University, Torbat-e Jam Branch, Torbat, Iran.

Maryam Saberi Anari

Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

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  • A. Rezaee, K. Rezaee, J. Haddadnia and H. T. Gorji, ...
  • F. Ozdemir, Z. Peng, P. Fuernstahl, C. Tanner, O. Goksel, ...
  • K. Rezaee, A. Badiei, S. Meshgini, “A hybrid deep transfer ...
  • K. Rezaee, A. Rezaee, N. Shaikhi, J. Haddadnia, “Multi-mass breast ...
  • T. Kim, and et al., “Active learning for accuracy enhancement ...
  • K. Rezaee, and et al, “Multi-mass breast cancer classification based ...
  • Y. Shi, M. Li, W. Zeng, “MARGM: A multi-subjects adaptive ...
  • T. Wu, Z. Yang, “Animal tumor medical image analysis based ...
  • Y. Alzahrani, Y., B. Boufama, “Biomedical Image Segmentation: A Survey”, ...
  • F. H. Araújo, R. R. Silva, F. N. Medeiros, J. ...
  • K. Bi, Y. Tan, K. Cheng, Q. Chen, Y. Wang, ...
  • G. Liu, G., and et al., “Superpixel-based active contour model ...
  • Y. Lei, and G. Weng, “A robust hybrid active contour ...
  • B. D. M. Zhang and Q. Li, “Deep active contour ...
  • Y. Yang, R. Wang, H. Ren, “Active contour model based ...
  • S. Husham, A. Mustapha, S. A. Mostafa, M. K. Al-Obaidi, ...
  • M. Sharif, U. Tanvir, E. U. Munir, M. A. Khan ...
  • J.A. Sethian, “Evolution, Implementation, and Application of Level Set and ...
  • D. Adalsteinsson, J.A. Sethian, “A Fast Level Set Method for ...
  • S. Osher, R. Fedkiw, “Level Set Methods and Dynamic Implicit ...
  • D Terzopoulos, D Metaxas. “Dynamic ۳D Models with Local and ...
  • J. Weickert and G. Kuhne, “Fast methods for implicit active ...
  • M. Holtzman-Gazit, D. Goldshe, and R Kimmel. “Hierarchical segmentation of ...
  • C. Rother, V. Kolmogorov, and A. Blake, “GrabCut: interactive foreground ...
  • C. Pluempitiwiriyawej, JMF. Moura, Yi-Jen Lin Wu and Chien Ho. ...
  • Y. Boykov and G. Funka-Lea, “Graph Cuts and Efficient N-D ...
  • Herbulot, S. Jehan-Besson, S. Duffner, M. Barlaud, and G. Aubert. ...
  • C.M. Li, C. Kao, J. Gore, Z. Ding, “Implicit active ...
  • K. Ni, X. Bresson, T. Chan, and S. Esedoglu. “Local ...
  • N. Le, T. Bui, V. K. Vo-Ho, K. Yamazaki, K. ...
  • K. Rezaee, S. M. Rezakhani, M. R. Khosravi, M. K. ...
  • K. Rezaee, S. Savarkar, X. Yu, J Zhang, “A hybrid ...
  • X. Chen, B. M. Williams, S. R. Vallabhaneni, G. Czanner, ...
  • S. Gur, L. Wolf, L. Golgher and P. Blinder, “Unsupervised ...
  • B. Kim and J. C. Ye, “Mumford–shah loss functional for ...
  • Y. Kim, S. Kim, T. Kim and C. Kim, “CNN-based ...
  • P. Hu, B. Shuai, J. Liu and G. Wang, “Deep ...
  • D. Marcos, D. Tuia, B. Kellenberger, L. Zhang, M. Bai, ...
  • A. Hatamizadeh, A. Hoogi, D. Sengupta, W. Lu, B. Wilcox, ...
  • E. E. Nithila and S. S. Kumar, “Segmentation of lung ...
  • J. Suckling, J. Parker, D. R. Dance, S. Astley, I. ...
  • L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. ...
  • P. Kohli, P. H. Torr and L. Ladick, “Robust higher ...
  • Y. Yang, X. Hou, H. Ren, “Efficient active contour model ...
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