A Semi-Automatic Algorithm for Estimating Cobb Angle

Automatic Cobb angle determination from radiographic images.

STUDY DESIGN Automatic measurement of Cobb angle in patients with scoliosis. OBJECTIVE To test the accuracy of an automatic Cobb angle determination method from frontal radiographical images. SUMMARY OF BACKGROUND DATA Thirty-six frontal radiographical images of patients with scoliosis. METHODS A modified charged particle model is used to determine the curvature on radiographical spinal i...

A semi-automatic algorithm for grey level estimation in tomography

Discrete tomography is a powerful approach for reconstructing images that contain only a few grey levels from their projections. Most theory and reconstruction algorithms for discrete tomography assume that the values of these grey levels are known in advance. In many practical applications, however, the grey levels are unknown and difficult to estimate. In this paper, we propose a semiautomati...

Automatic Cobb Angle Measurement System by Using Nuclear Medicine Whole Body Bone Scan

Evaluation of scoliosis is performed traditionally by means of standing posterior-anterior radiographs of the full spine to assess lateral curvature with the Cobb method. In this paper, we present a new automatic algorithm to measure Cobb angle by using nuclear medicine whole body bone scan images. Our algorithm design is based on the fuzzy sets histogram thresholding, anatomical knowledge-base...

Cobb Angle Quantification for Scoliosis using Image Processing Techniques

Measurement of Cobb angle is the standard technique used for scoliosis assessment. The challenging task in computerized method lies in totally automating the method of curvature measurement from digital X-ray images. In this paper we presented a method which automatically measures the Cobb angle from radiographs after selection of the end vertebrae of the curve. The image processing methods use...

Evaluating a semi-automatic sugar-beet steckling transplanter performance

Fast semi-automatic segmentation algorithm for Self-Organizing Maps

Self-Organizing Maps (SOM) are very powerful tools for data mining, in particular for visualizing the distribution of the data in very highdimensional data sets. Moreover, the 2D map produced by SOM can be used for unsupervised partitioning of the original data set into categories, provided that this map is somehow adequately segmented in clusters. This is usually done either manually by visual...