Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review
Authors: 
Karen Carrasco, Lenin Tomalá, Eileen Ramírez Meza, Doris Meza Bolaños, Washington Ramírez MontalvanAuthors Info & Claims
Karen Carrasco, Lenin Tomalá, Eileen Ramírez Meza, Doris Meza Bolaños, Washington Ramírez MontalvanAuthors Info & ClaimsArticle No.: 197, Pages 1 - 38
Abstract
The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of literature poses an obstacle to a complete understanding in this area of interest. The methodologies studied were systematic mapping and systematic literature review. For each article, the journal, conference, year of publication, dataset, breast cancer characteristics, PET/CT processing techniques, metrics and diagnostic yield results were identified. Sixty-four articles were analyzed, 44 (68.75%) belong to journals and 20 (31.25%) belong to the conference category. A total of 102 techniques were identified, which were distributed in preprocessing with 7 (6.86%), segmentation with 15 (14.71%), feature extraction with 15 (14.71%), and classification with 65 (63.73%). The techniques with the highest incidence identified in each stage are: Gaussian Filter, SLIC, Local Binary Pattern, and Support Vector Machine with 4, 2, 7, and 35 occurrences, respectively. Support Vector Machine is the predominant technique in the classification stage, due to the fact that Artificial Intelligence is emerging in medical image processing and health care to make expert systems increasingly intelligent and obtain favorable results.
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Index Terms
- Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review
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Publication History
Published: 26 April 2024
Online AM: 28 February 2024
Accepted: 31 January 2024
Revised: 11 January 2024
Received: 11 March 2023
Published in CSUR Volume 56, Issue 8
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