Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with
key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An
automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility
should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the
landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is
segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the
boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour
of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse
plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3
healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two
manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the
orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.
In most magnetic resonance imaging (MRI) clinical examinations, the orientation and position of diagnostic scans are
manually defined by MRI operators. To accelerate the workflow, algorithms have been proposed to automate the
definition of the MRI scanning planes. A mid-sagittal plane (MSP), which separates the two cerebral hemispheres, is
commonly used to align MRI neurological scans, since it standardizes the visualization of important anatomy. We
propose an algorithm to define the MSP automatically based on lines separating the cerebral hemispheres in 2D coronal
and transverse images. Challenges to the automatic definition of separation lines are disturbances from the inclusion of
the shoulder, and the asymmetry of the brain. The proposed algorithm first detects the position of the head by fitting an
ellipse that maximizes the image gradient magnitude in the boundary region of the ellipse. A symmetrical axis is then
established which minimizes the difference between the image on either side of the axis. The pixels at the space between
the hemispheres are located in the adjacent area of the symmetrical axis, and a linear regression with robust weights
defines a line that best separates the two hemispheres. The geometry of MSP is calculated based on the separation lines
in the coronal and transverse views. Experiments on 100 images indicate that the result of the proposed algorithm is
consistent with the results obtained by domain experts and is significantly faster.
Rheumatoid Arthritis is one of the most common chronic diseases. Joint space width in hand radiographs is evaluated to assess joint damage in order to monitor progression of disease and response to treatment. Manual measurement of joint space width is time-consuming and highly prone to inter- and intra-observer variation. We propose a method for automatic extraction of finger bone boundaries using fast marching methods for quantitative evaluation of joint space width. The proposed algorithm includes two stages: location of hand joints followed by extraction of bone boundaries. By setting the propagation speed of the wave front as a function of image intensity values, the fast marching algorithm extracts the skeleton of the hands, in which each branch corresponds to a finger. The finger joint locations are then determined by using the image gradients along the skeletal branches. In order to extract bone boundaries at joints, the gradient magnitudes are utilized for setting the propagation speed, and the gradient phases are used for discriminating the boundaries of adjacent bones. The bone boundaries are detected by searching for the fastest paths from one side of each joint to the other side. Finally, joint space width is computed based on the extracted upper and lower bone boundaries. The algorithm was evaluated on a test set of 8 two-hand radiographs, including images from healthy patients and from patients suffering from arthritis, gout and psoriasis. Using our method, 97% of 208 joints were accurately located and 89% of 416 bone boundaries were correctly extracted.
Forensic odontology has long been carried out by forensic experts of law enforcement agencies for postmortem identification. We address the problem of developing an automated system for postmortem identification using dental records (dental radiographs). This automated dental identification system (ADIS) can be used by law enforcement agencies as well as military agencies throughout the United States to locate missing persons using databases of dental x rays of human remains and dental scans of missing or wanted persons. Currently, this search and identification process is carried out manually, which makes it very time-consuming in mass disasters. We propose a novel architecture for ADIS, define the functionality of its components, and describe the techniques used in realizing these components. We also present the performance of each of these components using a database of dental images.
The human dental atlas contains a detailed description of each tooth in the mouth and their relative positions. Registering a dental radiograph to the dental atlas reveals the position and index of each tooth in the radiograph. This helps in establishing the correspondence of teeth when matching two radiographs for human identification. We propose a hidden Markov model (HMM) as an underlying representation of the dental atlas. In our model, the states representing the available teeth have discrete observations, namely the class of each tooth, and the states representing the missing teeth have continuous observations-the distance between neighboring teeth. To classify the teeth, three support vector machines (SVMs) using different feature sets are combined using the average fusion method. Experimental results show that this registration algorithm is promising.