Weighted level set evolution based on local edge features for medical image segmentation

Alaa Khadidos, Victor Sanchez, Chang Tsun Li

    Research output: Contribution to journalArticlepeer-review

    120 Citations (Scopus)

    Abstract

    Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image's gradient vector flow field and the evolving contour's normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based level set segmentation methods, particularly around weak edges.
    Original languageEnglish
    Article number7847297
    Pages (from-to)1979-1991
    Number of pages13
    JournalIEEE Transactions on Image Processing
    Volume26
    Issue number4
    DOIs
    Publication statusPublished - Apr 2017

    Fingerprint

    Dive into the research topics of 'Weighted level set evolution based on local edge features for medical image segmentation'. Together they form a unique fingerprint.

    Cite this