Computer aided detection (CADe) systems often present multiple false-positives per image in projection
mammography due to overlapping anatomy. To reduce the number of such false-positives, we propose
performing CADe on image pairs acquired using a bi-plane correlation imaging (BCI) technique. In this
technique, images are acquired of each breast at two different projection angles. A traditional CADe
algorithm operates on each image to identify suspected lesions. The suspicious areas from both projections
are then geometrically correlated, eliminating any lesion that is not identified on both views. Proof of concept
studies showed that that the BCI technique reduced the numbers of false-positives per case up to 70%.
In this paper, we present performance of biplane correlation imaging (BCI) on set of chest x-ray projections of human data. BCI significantly minimizes the number of false positives (FPs) when used in conjunction with computer aided detection (CAD) by eliminating non-correlated nodule candidates. Sixty-one low exposure posterior projections were acquired from more than 20 human subjects with small angular separations (0.32 degree) over a range of 20 degrees along the vertical axis. All patients were previously diagnosed for the presence of lung nodules based on computed tomography (CT) examination. Images were processed following two steps. First, all images were analyzed using our CAD routine for chest radiography. This process proceeded with a BCI processing in which the results of CAD on each single projection were examined in terms of their geometrical correlation with those found in the other 60 projections based on the predetermined shift of possible nodule locations in each projection. The suspect entities with a geometrical correlation that coincided with the known location of the lesions were selected as nodules; otherwise they were ignored. An expert radiologist with reference to the associated CT dataset determined the truth regarding nodule location and sizes, which were then used to determine if the found nodules are true positive or false positive. The preliminary results indicated that the best performance was obtained when the angular separation of the projection pair was greater than about 6.7 degrees. Within the range of optimum angular separation, the number of FPs per image was 0-1 without impacting the number of true positives (TPs), averaged around 92%.
In this paper, we evaluate the performance of biplane correlation imaging (BCI) using a set of off-angle projections acquired from an anthropomorphic chest phantom. BCI reduces the effect of anatomical noise, which would otherwise impact the detection subtle lesions in planar images. BCI also minimizes the number of false positives (FPs) when used in conjunction with computer aided diagnosis (CAD) applied to a set of coronal chest x-ray projections by eliminating non-correlated nodule candidates. In BCI, two digital images of the chest are acquired within a short time interval from two slightly different posterior projections. The image data are then incorporated into the CAD algorithm in which nodules are detected by examining the geometrical correlation of the detected signals in the two views, thus largely "canceling" the impact of anatomical noise. Seventy-one low exposure posterior projections were acquired of an anthropomorphic chest phantom containing tissue equivalent lesions with small angular separations (0.32 degree) over a range of 20 degrees, [-10°, +10°], along the vertical axis. The data were analyzed to determine the accuracy of the technique as a function of angular separation. The results indicated that the best performance was obtained when the angular separation of the projection pair was greater than 6 degrees. Within the range of optimum angular separation, the number of FPs per image, FPpI, was ~1.1 with average sensitivity around 75% (supported by a grant from the NIH R01CA109074).
We present new approaches based on Genetic Algorithms (GAs), Simulated Annealing (SA) and Expectation Maximization (EM) for learning parameters of the mixture of Gaussian model. GAs are adaptive search techniques designed to search for near optimal solutions of large-scale optimization problems with multiple local maxima. It has been shown that GAs are independent of initialization parameters and can provide an efficient technique to optimize functions in large search spaces while the solution obtained by EM is a function of initial parameters, hence relatively high likelihood of achieving sub-optimal solution, due to trapping in local maxima.
In this work we propose a new incorporate genetic algorithm with EM (Interactive GA-EM) to improve estimation of Gaussian mixture parameters. The method uses a population of mixture models, rather than a single mixture, interactively in both GA and EM to determine Gaussian mixture parameters. To assess the performance of the proposed methods, a series of Gaussian phantoms, based on modified Shepp-Logan method, were created. All proposed methods were employed to estimate the tissue parameters in each phantom. The results indicate that the EM algorithm, as expected, is heavily impacted by the initial values. The best result on both computational time and accuracy was obtained from Interactive GA-EM.
The proposed method offers an accurate and stable solution for parameter estimation on Gaussian mixture models, with higher chance of achieving global optimal. Obtaining such accurate parameter estimation is a key requirement for several image segmentation approaches, which rely on a priori knowledge of tissue distribution.
We present a new approach for estimating parameters of Gaussian mixture model by Genetic Algorithms (Gas) and Expectation Maximization (EM). It has been shown that Gas is independent of initialization parameters. In this work we propose combination of Gas and EM algorithms (GA-EM) for learning Gaussian mixture components to achieve accurate parameter estimation independent of initial values. To assess the performance of the proposed method, a series of Gaussian phantoms, based on modified Shepp-Logan method, were created. In this phantom, each tissue segment presents a Gaussian density function that its mean and variance can be controlled. EM, Gas and GAs-EM were employed to estimate the tissue parameters in each phantom. The results indicate that EM algorithm, as expected is heavily impacted by the initial values. Coupling Gas with EM not only improves the overall accuracy, it also provides estimates that are independent of initial seed values. The proposed method offers a solution for accurate and stable solution for parameter estimation in for Gaussian mixture models, with higher likelihood of achieving global optimal. Obtaining such accurate parameter estimation is a key requirement for several image segmentation approaches, which rely on a priori knowledge of tissue distribution.
This paper proposes a computationally efficient hierarchical technique for object detection and segmentation and compare it with two other segmentation algorithms. The segmentation (MR) algorithm is performed at coarse resolution based on a maximum a posteriori (MAP) estimation of the field of pixel classifications, which is modeled a Markov random field (MRF). MR performs segmentation of a given image at coarse resolutions. Each resolution will correspond to a hierarchical level in a quad tree. So the classification of a pixel at one resolution will correspond to the classification of four pixels at the next finer resolution. Using this relationship we segment the image at the coarse resolution, each pixel in coarse resolution can be related to 16 pixels in the finer resolution. To find minimum global energy at coarse resolution, one pixel from 16 of observed image field given the unobserved filed. The MAP estimates the pixel classes given the observed filed. Segmentation process at each individual pixel will be performed by searching randomly in each relative pixel at 4x4 block-pixel to find minimum global energy at coarse resolution. Images from simulated head phantoms, degraded by Gaussian noise, are used for comparison of the proposed method with simulated annealing (SA) and minimum gray level distance (MGLD) approaches. Computational cost and segmentation accuracy of these methods are studied. It is shown that the proposed MR method offers a robust and computationally inexpensive method for segmentation of noisy images.
The transducer driving function for Bessel beam has circular symmetry and can be generated by annular or 2-D arrays. In 2-D array, the elements are divided into a number of rings. Taking advantage of the circular symmetry, it was shown that arranging the elements in a hexagonal pattern instead of ordinary rectangular pattern could produce almost the same field pattern with 14% less elements. Our aim here is to eliminate some of the elements of the hexagonal array and obtain a hexagonal sparse array while maintaining the quality of the generated field. In our proposed method, starting from the outer most ring, a specific number of the elements of the ring are randomly selected and turned off. The field pattern of the resulting sparse arrays is simulated and compared to the field of the array with all of its elements active. If the relative mean square error is lower than a specific threshold value, more elements of the ring are turned off. This procedure is then repeated for the next ring until reaching the central ring. Our simulations for hexagonal sparse arrays show that for an error threshold of 4%, an acceptable Bessel beam can be generated only with 22% of the transducer elements used in the original hexagonal arrays. Generated beam still shows its non-diffracting property over a limited distance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.