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Critical Reviews™ in Biomedical Engineering
SJR: 0.243 SNIP: 0.376 CiteScore™: 0.79

ISSN Imprimir: 0278-940X
ISSN En Línea: 1943-619X

Volumes:
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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.v36.i5-6.10
pages 305-334

Segmentation of Images of Abdominal Organs

Jie Wu
Departments of Computing and Software, , School of Biomedical Engineering; , McMaster University, Hamilton, Ontario, Canada
Markad V. Kamath
Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, Ontario, L8N 3Z5 Canada
Michael D. Noseworthy
McMaster School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada; Imaging Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada; Department of Radiology, McMaster University, Hamilton, Ontario, Canada; Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada
Colm Boylan
Department of Radiology, McMaster University and St. Joseph's Health Care Hamilton
Skip Poehlman
Departments of Computing and Software, McMaster University, Hamilton, Ontario, Canada

SINOPSIS

Abdominal organ segmentation, which is, the delineation of organ areas in the abdomen, plays an important role in the process of radiological evaluation. Attempts to automate segmentation of abdominal organs will aid radiologists who are required to view thousands of images daily. This review outlines the current state-of-the-art semi-automated and automated methods used to segment abdominal organ regions from computed tomography (CT), magnetic resonance imaging (MEI), and ultrasound images. Segmentation methods generally fall into three categories: pixel based, region based and boundary tracing. While pixel-based methods classify each individual pixel, region-based methods identify regions with similar properties. Boundary tracing is accomplished by a model of the image boundary. This paper evaluates the effectiveness of the above algorithms with an emphasis on their advantages and disadvantages for abdominal organ segmentation. Several evaluation metrics that compare machine-based segmentation with that of an expert (radiologist) are identified and examined. Finally, features based on intensity as well as the texture of a small region around a pixel are explored. This review concludes with a discussion of possible future trends for abdominal organ segmentation.