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Automated segmentation of abdominal organs from contrast‐enhanced computed tomography using analysis of texture features
Author(s) -
Danilov Alexander,
Yurova Alexandra
Publication year - 2020
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3309
Subject(s) - segmentation , artificial intelligence , computer science , robustness (evolution) , computer vision , image segmentation , computer graphics , graphics processing unit , medical imaging , computed tomography , contrast (vision) , computer aided diagnosis , modality (human–computer interaction) , image texture , image processing , surgical planning , pattern recognition (psychology) , radiology , image (mathematics) , medicine , biochemistry , chemistry , gene , operating system
Generation of three‐dimensional personalized geometric models of anatomical structures is an important process for many practical tasks: computer‐aided diagnosis, treatment planning and numerical modeling in biomedical applications. Despite many efforts done by different research groups, automatic segmentation of organs still does not have any general solution. The main difficulties are caused by peculiarities of different medical imaging modalities, image variability (for the same modality) resulting from the wide range of imaging devices, noise and artifacts, large patient anatomical variability and overlapping of intensity ranges of neighboring anatomical structures. In this article, we propose segmentation method based on analysis of texture features and developed specially for segmentation of abdominal organs. Its main advantage is robustness to interpatient gray level and anatomical variability. The proposed method was validated on the patient data. The method implementation was accelerated using graphics processing unit (GPU).