Radiographic assessment of joint space narrowing in hand radiographs is important for determining the progression
of rheumatoid arthritis in an early stage. Clinical scoring methods are based on manual measurements that
are time consuming and subjected to intra-reader and inter-reader variance. The goal is to design an automated
method for measuring the joint space width with a higher sensitivity to change<sup>1</sup> than manual methods. The large
variability in joint shapes and textures, the possible presence of joint damage, and the interpretation of projection
images make it difficult to detect joint margins accurately. We developed a method that uses a modified
active shape model to scan for margins within a predetermined region of interest. Possible joint space margin
locations are detected using a probability score based on the Mahalanobis distance. To prevent the detection of
false edges, we use a dynamic programming approach. The shape model and the Mahalanobis scoring function
are trained with a set of 50 hand radiographs, in which the margins have been outlined by an expert.
We tested our method on a test set of 50 images. The method was evaluated by calculating the mean absolute
difference with manual readings by a trained person. 90% of the joint margins are detected within 0.12 mm. We
found that our joint margin detection method has a higher precision considering reproducibility than manual
readings. For cases where the joint space has disappeared, the algorithm is unable to estimate the margins. In
these cases it would be necessary to use a different method to quantify joint damage.
Robust and accurate segmentation methods are important for the computerized evaluation of medical images. For treatment of rheumatoid arthritis, joint damage assessment in radiographs of hands is frequently used for monitoring disease progression. Current clinical scoring methods are based on visual measurements that are time-consuming and subject to intra and inter-reader variance. A solution may be found in the development of partially automated assessment procedures. This requires reliable segmentation algorithms. Our work demonstrates a segmentation method based on multiple connected active appearance models (AAM) with multiple search steps using different quality levels. The quality level can be regulated by setting the image resolution and the number of landmarks in the AAMs. We performed experiments using two models of different quality levels for shape and texture information. Both models included AAMs for the carpal region, the metacarpals, and all phalanges. By starting an iterative search with the faster, low-quality model, we were able to determine the initial parameters of the second, high-quality model. After the second search, the results showed successful segmentation for 22 of 30 test images. For these images, 70% of the landmarks were found within 1.3 mm difference from manual placement by an expert. The multi-level search approach resulted in a reduction of 50% in calculation time compared to a search using a single model. Results are expected to improve when the model is refined by increasing the number of training examples and the resolution of the models.
The purpose of our research is to describe the ultimate X-ray detector for angiography. Angiography is a well established X-ray imaging technique for the examination of blood vessels. Contrast agent is injected followed by X-ray exposures and possible obstructions in the blood vessels can be visualized. Standard angiography primarily inspects for possible occlusions and views the vessels as rigid pipes. However, due to the beating heart the flow in arteries is pulsatile. Healthy arteries are not rigid tubes but adapt to various pressure and flow conditions. Our interest is in the (small) response of the artery on the pulse flow. If the arteries responses elastically on the pulse flow, we can expect that it is still healthy. So the detection of artery diameter variations is of interest for the detection of atherosclerosis in an early stage. In this contribution we specify and test a model X-ray detector for its abilities to record the responses of arteries on pulsatile propagating flow distributions. Under normal physiological conditions vessels respond with a temporal increase in arterial internal cross-sectional area of order 10%. This pulse flow propagates along the arteries in response of the left ventricle ejections. We show results of the detection of simulated vessel distensabilities for the model detector and discuss salient parameters features.
This paper describes the design, implementation and evaluation of a user-verification system for a smart gun,
which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 × 44 piezoresistive
elements is used to measure the grip pattern. An interface has been developed to acquire pressure images
from the sensor. The values of the pixels in the pressure-pattern images are used as inputs for a verification
algorithm, which is currently implemented in software on a PC. The verification algorithm is based on a likelihoodratio
classifier for Gaussian probability densities. First results indicate that it is feasible to use grip-pattern
recognition for biometric verification.