Automated seed detection and three-dimensional reconstruction. I. Seed localization from fluoroscopic images or radiographs.

An automated procedure for the detection of the position and the orientation of radioactive seeds on fluoroscopic images or scanned radiographs is presented. The extracted positions of seed centers and the orientations are used for three-dimensional reconstruction of permanent prostate implants. The extraction procedure requires several steps: correction of image intensifier distortions, normalization, background removal, automatic threshold selection, thresholding, and finally, moment analysis and classification of the connected components. The algorithm was tested on 75 fluoroscopic images. The results show that, on average, 92% of the seeds are detected automatically. The orientation is found with an error smaller than 50 for 75% of the seeds. The orientation of overlapping seeds (10%) should be considered as an estimate at best. The image processing procedure can also be used for seed or catheter detection in CT images, with minor modifications.

Automated seed detection and three-dimensional reconstruction. II. Reconstruction of permanent prostate implants using simulated annealing.

We present an algorithm, based on simulated annealing, for automatic seed matching and three-dimensional spatial coordinate reconstruction using either three radiographic films or three fluoroscopic images taken from different perspectives. The matching problem is defined in the framework of combinatorial optimization, which allows robust reconstruction in presence of calibration imprecision, patient movements, and isometric distortions. Furthermore, by using a global criterion to select the correct matching, we evade common problems of the three-film method and its variants in presence of noise. The algorithm has been tested on 112 clinical cases and 100 simulated implants and used clinically on more than 100 cases. Simulated implants were reconstructed with an average error of 0.21 mm. For clinical cases, comparison of the precision is performed between results obtained with this new method and results obtained using the three-film technique. Compared to the latter technique, the reconstruction precision was improved in 62% of the clinical cases.

Automatic setup deviation measurements with electronic portal images for pelvic fields.

The purpose of this work was to develop a fully automatic tool for the detection of setup deviation for small pelvic field using, in external beam radiotherapy, an electronic portal imaging device (EPID). The algorithm processes electronic portal images of prostate cancer patients. No fiducial points or user interventions are needed. Deviation measurements are based on bone edge detection performed with Laplacian of a Gaussian (LoG) operator. Two bone edge images are then correlated, one of which is a reference image taken as the first fraction image for the purpose of this study. The electronic portal images (EPI) also show band artefacts which are removed using the morphological top-hat transform. The algorithm was first validated with 59 phantom images acquired in clinical treatment conditions with known displacements. The algorithm was then validated with 79 clinical images where bone contours were delineated manually. For the phantom images, the setup deviations were measured with a absolute mean error of 0.59 mm and 0.47 mm with a standard deviation of 0.64 mm and 0.42 mm, horizontally and vertically, respectively. A second validation was performed using clinical prostate cancer images. The measured patient displacements have an absolute mean error of 0.48 mm and 1.41 mm with a standard deviation of 0.58 mm and 1.30 mm in the X and Y directions, respectively. The algorithm execution time on a SUN workstation is 5 s. This algorithm shows good potential as a setup deviation measurement tool in clinical practice. The possibility of using this algorithm combined with decision rules based on statistical observations is very promising.

A novel approach for coding color quantized images.

An approach to the lossy compression of color images with limited palette that does not require color quantization of the decoded image is presented. The algorithm is particularly suited for coding images using an image-dependent palette. The technique restricts the pixels of the decoded image to take values only in the original palette. Thus, the decoded image can be readily displayed without having to be quantized. For comparable quality and bit rates, the technique significantly reduces the decoder computational complexity.