Object-based analysis of CT images for automatic detection and segmentation of hypodense liver lesions

• Published in: International journal for computer assisted radiology and surgery Vol. 6; no. 6; pp. 737 - 747
• Main Authors: Schwier, Michael, Moltz, Jan Hendrik, Peitgen, Heinz-Otto
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.11.2011
• Subjects: Algorithms; Computer Imaging; Computer Science; Health Informatics; Humans; Imaging; Imaging, Three-Dimensional; Liver Diseases - diagnostic imaging; Liver Diseases - pathology; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Pattern Recognition, Automated - methods; Radiology; Surgery; Tomography, X-Ray Computed - methods; Vision; Liver tumors; Lesion segmentation; Computer-aided detection; Liver lesions; Object-based image analysis
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-011-0562-8

Purpose Hypodense liver lesions are commonly detected in CT, so their segmentation and characterization are essential for diagnosis and treatment. Methods for automatic detection and segmentation of liver lesions were developed to support this task. Methods The detection algorithm uses an object-based image analysis approach, allowing for effectively integrating domain knowledge and reasoning processes into the detection logic. The method is intended to succeed in cases typically difficult for computer-aided detection systems, especially low contrast of hypodense lesions relative to healthy tissue. The detection stage is followed by a dedicated segmentation algorithm needed to synthesize 3D segmentations for all true-positive findings. Results The automated method provides an overall detection rate of 77.8% with a precision of 0.53 and performs better than other related methods. The final lesion segmentation delivers appropriate quality in 89% of the detected cases, as evaluated by two radiologists. Conclusions A new automated liver lesion detection algorithm employs the strengths of an object-based image analysis approach. The combination of automated detection and segmentation provides promising results with potential to improve diagnostic liver lesion evaluation.