Computer assisted detection of abnormal airway variation in CT scans related to paediatric tuberculosis.

Airway deformation and stenosis can be key signs of pathology such as lymphadenopathy. This study presents a local airway point distribution model (LA-PDM) to automatically analyse regions of the airway tree in CT scans and identify abnormal airway deformation. In our method, the airway tree is segmented and the centreline identified from each chest CT scan.

An in vivo subject-specific 3D functional knee joint model using combined MR imaging.

Purpose: We aim to quantitatively characterise the knee joint function in vivo under body-weight-bearing conditions via subject-specific models extracted from magnetic resonance (MR) data, in order to better understand the knee joint kinematic function in 3D.

Methods: Six healthy volunteers without any record of knee abnormality were scanned using a combined MR imaging strategy to record quasi-squatting motion and 3D knee anatomy. After a semi-automatic segmentation to delineate tibio-femoral articulation components, motion data were mapped to the anatomical data using a bi-rigid registration in order to achieve six degrees of freedom.

Identification of paediatric tuberculosis from airway shape features.

Clinical signs of paediatric pulmonary tuberculosis (TB) include stenosis and deformation of the airways. This paper presents two methods to analyse airway shape and detect airway pathology from CT images. Features were extracted using (1) the principal components of the airway surface mesh and (2) branch radius and orientation features.

2D/3D fetal cardiac dataset segmentation using a deformable model.

Purpose: To segment the fetal heart in order to facilitate the 3D assessment of the cardiac function and structure.

Methods: Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. The authors outline a level set deformable model to automatically delineate the small fetal cardiac chambers.

Texture-based quantification of lumbar intervertebral disc degeneration from conventional T2-weighted MRI.

Background: Disc degeneration quantification is important for monitoring the effects of new therapeutic methods, such as cell and growth factor therapy. Magnetic resonance (MR) image texture reflects biochemical and structural tissue properties and has been used for differentiating between normal and pathological status in a variety of medical applications.

Purpose: To investigate the suitability of textural descriptors for the quantification of intervertebral disc degeneration using conventional T2-weighted magnetic resonance images of the lumbar spine.