KEYWORDS: CAD systems, Detection and tracking algorithms, Solid modeling, Cameras, Visual process modeling, Image enhancement, Imaging systems, Sensors, Visualization, Human vision and color perception
An automatic landing site detection algorithm is proposed for aircraft emergency landing. Emergency landing
is an unplanned event in response to emergency situations. If, as is unfortunately usually the case, there is
no airstrip or airfield that can be reached by the un-powered aircraft, a crash landing or ditching has to be
carried out. Identifying a safe landing site is critical to the survival of passengers and crew. Conventionally,
the pilot chooses the landing site visually by looking at the terrain through the cockpit. The success of this
vital decision greatly depends on the external environmental factors that can impair human vision, and on
the pilot's flight experience that can vary significantly among pilots. Therefore, we propose a robust, reliable
and efficient algorithm that is expected to alleviate the negative impact of these factors. We present only the
detection mechanism of the proposed algorithm and assume that the image enhancement for increased visibility,
and image stitching for a larger field-of-view have already been performed on the images acquired by aircraftmounted
cameras. Specifically, we describe an elastic bound detection method which is designed to position
the horizon. The terrain image is divided into non-overlapping blocks which are then clustered according to a
"roughness" measure. Adjacent smooth blocks are merged to form potential landing sites whose dimensions are
measured with principal component analysis and geometric transformations. If the dimensions of the candidate
region exceed the minimum requirement for safe landing, the potential landing site is considered a safe candidate
and highlighted on the human machine interface. At the end, the pilot makes the final decision by confirming
one of the candidates, also considering other factors such as wind speed and wind direction, etc. Preliminary
results show the feasibility of the proposed algorithm.
A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete
MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image
(DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and
tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of
the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal,
tumor, and normal but progressed to tumor at a later time. In this preliminary study, we focused on the trend of brain
tumor progression during three consecutive visits, i.e., visit A, B, and C. A machine learning algorithm was trained using
the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from
visit A to visit C. Preliminary results showed that prediction for brain tumor progression is feasible. An average of
80.9% pixel-wise accuracy was achieved for tumor progression prediction at visit C.
Analysis of ultrasound fetal head images is a daily routine for medical professionals in obstetrics. The contours of
fetal skulls often appear discontinuous and irregular in clinical ultrasound images, making it difficult to measure
the fetal head size automatically. In addition, the presence of heavy noise in ultrasound images is another
challenge for computer aided automatic fetal head detection. In this paper, we first utilize the stick method to
suppress the noise and compute an adaptive threshold for fetal skull segmentation. Morphological thinning is then
performed to obtain a skeleton image, which is used as an input to the Hough transform. Finally, automatic fetal
skull detection is realized by Iterative Randomized Hough Transform (IRHT). The elliptic eccentricity is used
in the IRHT to reduce the number of invalid accumulations in the parameter space, improving the detection
accuracy. Furthermore, the target region is adaptively adjusted in the IRHT. To evaluate the performance
of IRHT, we also developed a simulation user interface for comparing results produced by the conventional
randomized Hough transform (RHT) and the IRHT. Experimental results showed that the proposed method is
effective for automatic fetal head detection in ultrasound images.
Purpose: Diffusion tensor imaging (DTI) is an inherently quantitative imaging technique that measures the
diffusivities of water molecules in tissue. However, the accuracy of DTI measurements depends on many
factors such S/N ratio and magnet field strength. Therefore, before quantitative assessment of tumor
progression based on DTI metric changes can be made with confidence, one have to assess the accuracy or
variance in the DTI metrics. This is especially important for multi-institutional clinical trials or for large
institutions where patients may be imaged on multiple MR scanners at multiple times in follow up studies.
In this presentation, we studied the feasibility of using CSF as an internal QC marker for data acquisition
and processing qualities. Method: ADC and FA of CSF for brain tumor patients' DTI studies (total of 85
scans over three years) were analyzed. In addition, a phantom was used to check the inherent variations of
the MR systems. Results: The results show that the coefficient of variations for ADC and FA are 8.4% and
13.2% in CSF among all patients. For all DTI scans done on 1.5 T scanners, they are 7.4% and 9.1%, while
for 3T they are 9.8% and 18% respectively. Conclusion: CSF can be used as an internal QC measure of the
DTI acquisition accuracy and consistency among longitude studies on patients, making it a potentially
useful in multi-institutional trials.
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