Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

被引:24
作者
Aloupogianni, Eleni [1 ]
Ishikawa, Masahiro [2 ]
Kobayashi, Naoki [3 ]
Obi, Takashi [1 ,4 ]
机构
[1] Tokyo Inst Technol, Dept Informat & Commun Engn, Tokyo, Japan
[2] Saitama Med Univ, Fac Hlth & Med Care, Saitama, Japan
[3] Saitama Med Univ, Fac Hlth & Med Care, Sch Biomed Engn, Saitama, Japan
[4] Tokyo Inst Technol, Inst Innovat Res, Lab Future Interdisciplinary Res Sci & Technol, Yokohama, Kanagawa, Japan
关键词
gross pathology; hyperspectral; classification; skin lesions; medical image processing; MELANOMA; PERFORMANCE; DIAGNOSIS; COLOR;
D O I
10.1117/1.JBO.27.6.060901
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Significance: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level. Aim: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue. Approach: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified. Results: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems. Conclusions: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.
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页数:28
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