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Unsupervised detection of liver lesions in CT images | IEEE Conference Publication | IEEE Xplore

Unsupervised detection of liver lesions in CT images


Abstract:

This work presents an automatic approach for liver lesions detection in CT images. In this approach, liver is first segmented using fast and reliable semi-automatic techn...Show More

Abstract:

This work presents an automatic approach for liver lesions detection in CT images. In this approach, liver is first segmented using fast and reliable semi-automatic technique. After liver segmentation, lesion detection is formulated as an unsupervised segmentation approach to alleviate tedious user interaction or prior learning requirements. The Meanshift clustering technique is utilized to separate different liver tissues in each CT slice. Consequently, a rule-based system is proposed to automatically and dynamically estimate healthy and unhealthy tissues distributions, and produces initial estimation of defected tissue. Finally, the graph cuts algorithm is employed to refine the initial detection and produces the finial lesions. Validation of the proposed approach using 15 patients' CT data shows high detection rate of 93%, which makes it an efficient initial opinion in the diagnosis process.
Date of Conference: 25-29 August 2015
Date Added to IEEE Xplore: 05 November 2015
ISBN Information:

ISSN Information:

PubMed ID: 26736780
Conference Location: Milan, Italy

References

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