Sichuan University

Zongan Liang

Junfeng Wang

Yulin Ji

Peng Cho Tang

Fan Li

Yihua Du

Lin Gao

A X-ray chest image rib suppression method based on Poisson model. It conducts contourlet transformation on the image and utilizes transformation coefficient correlation between different scales to conduct texture enhancement on the image; it designs strip-type detection filter in accordance with the Hessian matrix eigenvalue to the image and detects the area where the rib locates in; it combines enhanced texture and rib area information, establishes and solves rib suppression Poisson model, realizing the rib suppression in the image. Anisotropy and contourlet transformation multi-direction feature is utilized, scale and coefficients direction information are combined and distinction degree between texture and noise improves, enhancing texture while restraining noise; it realizes ribs suppression through solving the Poisson model, which does not need to conduct accurate segmentation on the rib, prevents unnatural transition problem of edges resulted from explicit ribs suppression and effectively suppress the ribs, improving observation effect of X-ray chest image.

Kind of x-ray chest image rib suppression method based on poisson model

A lung lobe contour extraction method aiming at DR radiography, which includes the steps: Obtain the representative lung lobe contour template through offline training; initialize the extraction system of contour lobe area in the chest DR radiography; According to dimension of DICOM image, conduct three-layer pyramid decomposition on the images; Use Gabor filter bank to rebuild pending images; Convert residual error of the rebuilt image by the Gabor filter into black and white image; Use Zhan-Suen refinement algorithm to refine the black and white image; Call every template of the offline training and use them as the convolution kernel operator, conduct convolution on the contour imagep; Filter the local optimum convolution value with optimum possibility from the convolution results and combine the evaluations; Generate the lung lobe contour shape in accordance with the upper and lower templates of optimum matching and its position.

Kind of lung lobe contour extraction method aiming at DR radiography

A DR radiography lung contour extraction method based on fully convolutional network, which includes the steps: Establish the fully convolutional network structure of lung contour segmentation; Conduct off-line training on the weighting parameters of the fully convolutional network; Read DR image and weighting parameters of the fully convolutional network; Input DR image into fully convolutional network and output segmentation results of image through network terminal with network layer-by-layer feedforward. Establish lung contour in accordance with segmentation results.

Growing community of inventors

Chengdu, China

Zongan Liang