Compact tracking of surgical instruments through structured markers.

Virtual and augmented reality surgery calls for reliable and efficient tracking of the surgical instruments in the virtual or real operating theatre. The most diffused approach uses three or more not aligned markers, attached to each instrument and surveyed by a set of cameras. However, the structure required to carry the markers does modify the instrument's mass distribution and can interfere with surgeon movements. To overcome these problems, we propose here a new methodology, based on structured markers, to compute the six degrees of freedom of a surgical instrument. Two markers are attached on the instrument axis and one of them has a stripe painted over its surface. We also introduce a procedure to compute with high accuracy the markers center on the cameras image, even when partially occluded by the instrument's axis or by other structures. Experimental results demonstrate the reliability and accuracy of the proposed approach. The introduction of structured passive markers can open new possibilities to accurate tracking, combining markers detection with real-time image processing.

Bayesian denoising in digital radiography: a comparison in the dental field.

We compared two Bayesian denoising algorithms for digital radiographs, based on Total Variation regularization and wavelet decomposition. The comparison was performed on simulated radiographs with different photon counts and frequency content and on real dental radiographs. Four different quality indices were considered to quantify the quality of the filtered radiographs. The experimental results suggested that Total Variation is more suited to preserve fine anatomical details, whereas wavelets produce images of higher quality at global scale; they also highlighted the need for more reliable image quality indices.

Compression and smart coding of offset and gain maps for intraoral digital x-ray sensors.

The response of indirect x-ray digital imaging sensors is often not homogenous on the entire surface area. In this case, calibration is needed to build offset and gain maps, which are used to correct the sensor output. The sensors of new generation are equipped with an on-board memory, which serves to store these maps. However, because of its limited dimension, the maps have to be compressed before saving them. This step is critical because of the extremely high compression rate required. The authors propose here a novel method to achieve such a high compression rate, without degrading the quality of the sensor output. It is based on quad tree decomposition, which performs an adaptive sampling of the offset and gain maps, matched with a RBF-based interpolation strategy. The method was tested on a typical intraoral radiographic sensor and compared with traditional compression techniques. Qualitative and quantitative results show that the method achieves a higher compression rate and produces images of superior quality. The method can be adopted also in different fields where a high compression rate is required.

Statistical based impulsive noise removal in digital radiography.

A new filter to restore radiographic images corrupted by impulsive noise is proposed. It is based on a switching scheme where all the pulses are first detected and then corrected through a median filter. The pulse detector is based on the hypothesis that the major contribution to image noise is given by the photon counting process, with some pixels corrupted by impulsive noise. Such statistics is described by an adequate mixture model. The filter is also able to reliably estimate the sensor gain. Its operation has been verified on both synthetic and real images; the experimental results demonstrate the superiority of the proposed approach in comparison with more traditional methods.

3D analysis of the body center of mass in rock climbing.

The purpose of the present study was to search for common patterns and for differences in climbing strategies in a group of recreational climbers. Twelve participants were involved in the study. Each participant climbed a simple indoor route consisting of a 3m horizontal shift followed by a 3m ascent for five times. Climbers could choose their own style, their preferred speed and holds. Their motion was recorded through motion capture based on passive markers. Results suggested that two main climbing strategies were used: the first preferring agility over force and the second preferring force over agility. We also found that our best climbers tried to minimize power during all trials.

A new real time filter for local exposure correction in panoramic radiographs.

A new real time filter for local exposure correction in panoramic radiographs is presented here. The filter, called PaRSEC, allows eliminating the exposure artifacts, mainly introduced by Automatic Exposure Control (AEC) systems. These artifacts reduce the image readability and its diagnostic utility. The PaRSEC filter operates a local exposure equalization, based on a reliable estimate of the column mean gray level. Qualitative and quantitative results are reported for typical panoramic radiographs. They show a complete removal of the artifacts. The method compares favorably with other classical methods targeted to exposure correction.

A neural network based method for optical patient set-up registration in breast radiotherapy.

Patient set-up optimization is required in breast-cancer radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. Opto-electronic systems allow implementing automatic procedures to minimize the positional mismatches of light-reflecting markers located on the patient surface with respect to a corresponding reference configuration. The same systems are used to detect the position of the irradiated body surface by means of laser spots; patient set-up is then corrected by matching the control points onto a CT based reference model through surface registration algorithms. In this paper, a non-deterministic approach based on Artificial Neural Networks is proposed for the automatic, real-time verification of geometrical set-up of breast irradiation. Unlike iterative surface registration methods, no passive fiducials are used and true real-time performance is obtained. Moreover, the non-deterministic modeling performed by the neural algorithm minimizes sensitivity to intra-fractional and inter-fractional non-rigid motion of the breast. The technique was validated through simulated activities by using reference CT data acquired on four subjects. Results show that the procedure is able to detect and reduce simulated set-up errors and revealed high reliability in patient position correction, even when the surface deformation is included in testing conditions.

Enhancing digital cephalic radiography with mixture models and local gamma correction.

We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under- and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.