Int J Comput Assist Radiol Surg - Vertebra identification using template matching modelmp and K-means clustering.


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RPOSE: Accurate vertebra detection and segmentation are essential steps for automating the diagnosis of spinal disorders. This study is dedicated to vertebra alignment measurement, the first step in a computer-aided diagnosis tool for cervical spine trauma. Automated vertebral segment alignment determination is a challenging task due to low contrast imaging and noise. A software tool for segmenting vertebrae and detecting subluxations has clinical significance. A robust method was developed and tested for cervical vertebra identification and segmentation that extracts parameters used for vertebra alignment measurement.METHODS: Our contribution involves a novel combination of a template matching method and an unsupervised clustering algorithm. In this method, we build a geometric vertebra mean model. To achieve vertebra detection, manual selection of the region of interest is performed initially on the input image. Subsequent preprocessing is done to enhance image contrast and detect edges. Candidate vertebra localization is then carried out by using a modified generalized Hough transform (GHT). Next, an adapted cost function is used to compute local voted centers and filter boundary data. Thereafter, a K-means clustering algorithm is applied to obtain clusters distribution corresponding to the targeted vertebrae. These clusters are combined with the vote parameters to detect vertebra centers. Rigid segmentation is then carried out by using GHT parameters. Finally, cervical spine curves are extracted to measure vertebra alignment.RESULTS: The proposed approach was successfully applied to a set of 66 high-resolution X-ray images. Robust detection was achieved in 97.5 % of the 330 tested cervical vertebrae.CONCLUSIONS: An automated vertebral identification method was developed and demonstrated to be robust to noise and occlusion. This work presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.

Resumo Limpo

rpose accur vertebra detect segment essenti step autom diagnosi spinal disord studi dedic vertebra align measur first step computeraid diagnosi tool cervic spine trauma autom vertebr segment align determin challeng task due low contrast imag nois softwar tool segment vertebra detect sublux clinic signific robust method develop test cervic vertebra identif segment extract paramet use vertebra align measurementmethod contribut involv novel combin templat match method unsupervis cluster algorithm method build geometr vertebra mean model achiev vertebra detect manual select region interest perform initi input imag subsequ preprocess done enhanc imag contrast detect edg candid vertebra local carri use modifi general hough transform ght next adapt cost function use comput local vote center filter boundari data thereaft kmean cluster algorithm appli obtain cluster distribut correspond target vertebra cluster combin vote paramet detect vertebra center rigid segment carri use ght paramet final cervic spine curv extract measur vertebra alignmentresult propos approach success appli set highresolut xray imag robust detect achiev test cervic vertebraeconclus autom vertebr identif method develop demonstr robust nois occlus work present first step toward autom computeraid diagnosi system cervic spine trauma detect

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