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International Journal of Computer Assisted Radiology and Surgery
Erschienen in: International Journal of Computer Assisted Radiology and Surgery 7/2016

01.07.2016 | Original Article

Improved segmentation of low-contrast lesions using sigmoid edge model

verfasst von: Amir Hossein Foruzan, Yen-Wei Chen

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2016

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Abstract

Purpose

The intensity profile of an image in the vicinity of a tissue’s boundary is modeled by a step/ramp function. However, this assumption does not hold in cases of low-contrast images, heterogeneous tissue textures, and where partial volume effect exists. We propose a hybrid algorithm for segmentation of CT/MR tumors in low-contrast, noisy images having heterogeneous/homogeneous or hyper-/hypo-intense abnormalities. We also model a smoothed noisy intensity profile by a sigmoid function and employ it to find the true location of boundary more accurately.

Methods

A novel combination of the SVM, watershed, and scattered data approximation algorithms is employed to initially segment a tumor. Small and large abnormalities are treated distinctly. Next, the proposed sigmoid edge model is fitted to the normal profile of the border. The estimated parameters of the model are then utilized to find true boundary of a tissue.

Results

We extensively evaluated our method using synthetic images (contaminated with varying levels of noise) and clinical CT/MR data. Clinical images included 57 CT/MR volumes consisting of small/large tumors, very low-/high-contrast images, liver/brain tumors, and hyper-/hypo-intense abnormalities. We achieved a Dice measure of \(0.83\,(\pm 0.07)\) and average symmetric surface distance of \(2.56\,(\pm 6.31)\) mm. Regarding IBSR dataset, we fulfilled Jaccard index of \(0.85\,(\pm 0.07)\). The average run-time of our code was \(154\,(\pm 71)\) s.

Conclusion

Individual treatment of small and large tumors and boundary correction using the proposed sigmoid edge model can be used to develop a robust tumor segmentation algorithm which deals with any types of tumors.
Vorheriger Artikel Shape-based acetabular cartilage segmentation: application to CT and MRI datasets
Nächster Artikel Landmark constellation models for medical image content identification and localization

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Metadaten
Titel
Improved segmentation of low-contrast lesions using sigmoid edge model
verfasst von
Amir Hossein Foruzan
Yen-Wei Chen
Publikationsdatum
01.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 7/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-015-1323-x

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