@article{M66B14A34,
title = "Automatic Segmentation of Femoral Cartilage in Knee MR Images using Multi-atlas-based Locally-weighted Voting",
journal = "Journal of KIISE, JOK",
year = "2016",
issn = "2383-630X",
doi = "",
author = "Hyeun A Kim,Hyeonjin Kim,Han Sang Lee,Helen Hong",
keywords = "knee MRI,femoral cartilage segmentation,femur segmentation,locally-weighted voting,multi-atlas segmentation",
abstract = "In this paper, we propose an automated segmentation method of femoral cartilage in knee MR images using multi-atlas-based locally-weighted voting. The proposed method involves two steps. First, to utilize the shape information to show that the femoral cartilage is attached to a femur, the femur is segmented via volume and object-based locally-weighted voting and narrow-band region growing. Second, the object-based affine transformation of the femur is applied to the registration of femoral cartilage, and the femoral cartilage is segmented via multi-atlas shape-based locally-weighted voting. To evaluate the performance of the proposed method, we compared the segmentation results of majority voting method, intensity-based locally-weighted voting method, and the proposed method with manual segmentation results defined by expert. In our experimental results, the newly proposed method avoids a leakage into the neighboring regions having similar intensity of femoral cartilage, and shows improved segmentation accuracy."
}