Paper Info
Title
Unsupervised Scoliosis Diagnosis via a Joint Recognition Method with Multifeature Descriptors and Centroids Extraction.
Abstract
To solve the problem of scoliosis recognition without a labeled dataset, an unsupervised method is proposed by combining the cascade gentle AdaBoost (CGAdaBoost) classifier and distance regularized level set evolution (DRLSE). The main idea of the proposed method is to establish the relationship between individual vertebrae and the whole spine with vertebral centroids. Scoliosis recognition can be transferred into automatic vertebral detection and segmentation processes, which can avoid the manual data-labeling processing. In the CGAdaBoost classifier, diversified vertebrae images and multifeature descriptors are considered to generate more discriminative features, thus improving the vertebral detection accuracy. After that, the detected bounding box represents an appropriate initial contour of DRLSE to make the vertebral segmentation more accurate. It is helpful for the elimination of initialization sensitivity and quick convergence of vertebra boundaries. Meanwhile, vertebral centroids are extracted to connect the whole spine, thereby describing the spinal curvature. Different parts of the spine are determined as abnormal or normal in accordance with medical prior knowledge. The experimental results demonstrate that the proposed method cannot only effectively identify scoliosis with unlabeled spine CT images but also have superiority against other state-of-the-art methods.
Year
DOI
Venue
2018
10.1155/2018/6213264
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Computer vision,AdaBoost,Computer science,Segmentation,Level set,Artificial intelligence,Vertebra,Classifier (linguistics),Discriminative model,Centroid,Minimum bounding box
Journal
2018
ISSN
Citations 
PageRank 
1748-670X
0
0.34
References 
Authors
12
5
Name
Order
Citations
PageRank
Liyuan Zhang102.03
Jiashi Zhao200.68
Huamin Yang31917.29
Zhengang Jiang4226.42
Qingliang Li501.35