Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

Author:

Carrasco Karen1ORCID,Tomalá Lenin1ORCID,Ramírez Meza Eileen2ORCID,Meza Bolaños Doris3ORCID,Ramírez Montalvan Washington1ORCID

Affiliation:

1. Salesian Polytechnic University, Quito, Ecuador

2. Pontifical Catholic University of Ecuador, Quito, Ecuador

3. Central University of Ecuador, Quito, Ecuador

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.

Publisher

Association for Computing Machinery (ACM)

Reference123 articles.

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3. MSP-Ecuador. 2018. Cifras de Ecuador Cancer de Mama. https://www.salud.gob.ec/cifras-de-ecuador-cancer-demama/. Accessed: 9/4/2024.

4. S. Abirami and P. Chitra. 2020. Energy-efficient edge based real-time healthcare support system. In dvances in Computers. Elsevier 117 (2020) 339–368. DOI:10.1016/bs.adcom.2019.09.007

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