We propose image processing techniques for the detection
of blood vessels in fundus images of the retina. The methods
include the design of a bank of directionally sensitive Gabor filters
with tunable scale and elongation parameters. Forty images of the
retina from the Digital Retinal Images for Vessel Extraction database
were used to evaluate the performance of the methods. The results
of blood vessel detection using inverted green-channel images were
compared with the corresponding manually segmented blood vessels.
High efficiency in the detection of blood vessels with the area
under the receiver operating characteristics curve of up to 0.96 was
achieved with a combination of Gabor filters at three scales.
Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly
missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application
of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms
taken prior to the detection of breast cancer. A method based on Gabor filters and phase portrait analysis was
used to detect initial candidates of sites of architectural distortion. A total of 386 regions of interest (ROIs) were
automatically obtained from 14 "prior mammograms", including 21 ROIs related to architectural distortion.
The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. The
average fractal dimension of the normal (false-positive) ROIs was higher than that of the ROIs with architectural
distortion. For the "prior mammograms", the best receiver operating characteristics (ROC) performance achieved
was 0.74 with the fractal dimension and 0.70 with fourteen texture features, in terms of the area under the ROC
Oriented feature detectors are fundamental tools in image understanding, as many images display information of interest in the form of oriented features. Several oriented feature detectors have been developed; some of the important families of oriented feature detectors are steerable filters and Gabor filters. In this work, design criteria and performance analysis are presented for the following oriented feature detectors: the Gaussian second-derivative steerable filter; the quadrature-pair Gaussian second-derivative steerable filter; the real Gabor filter; the complex Gabor filter; and a line operator that has been shown to outperform the Gaussian second-derivative steerable filter in the detection of curvilinear structures in mammograms. The detectors are assessed in terms of their capability to detect the presence of oriented features as well as their accuracy in the estimation of the angle of the oriented features present in test images. It is shown that the real Gabor filter yields the best detection performance and angular accuracy, whereas the line operator and the steerable filter provide an advantage in terms of computational speed.
Oriented patterns in an image often convey important information regarding the scene or the objects contained. Given an image presenting oriented texture, the orientation field of the image is a map that depicts the orientation angle of the texture at each pixel. Rao and Jain developed a method to describe oriented patterns in an image based on the association between the orientation field of a textured image and the phase portrait generated by a pair of linear first-order differential equations. The estimation of the model parameters is a nonlinear, nonconvex optimization problem, and practical experience shows that irrelevant local minima can lead to convergence to inappropriate results. We investigated the performance of four optimization algorithms for the estimation of the optimal phase portrait parameters for a given orientation field. The investigated algorithms are: nonlinear least-squares, linear least-squares, iterative linear least-squares, and simulated annealing. The algorithms are evaluated and compared in terms of the error between the estimated parameters and the parameters known by design, in the presence of noise in the orientation field and imprecision in the initialization of the parameters. The computational effort required by each algorithm is also assessed. Individually, the simulated annealing procedure yielded low fixed-point and parameter errors over the entire range of noise tested, whereas the performance of the other methods deteriorated with higher levels of noise. The use of the result of simulated annealing for the initialization of the nonlinear least-squares method led to further improvement upon the simulated annealing results.
Oriented patterns in an image often carry important information about the scene represented. Rao and Jain developed a technique to analyze images with oriented texture using phase portraits, where the parameters of a planar first-order phase portrait are locally estimated using a nonlinear least-squares algorithm. The method gives accurate results, but is computationally expensive. Shu and Jain proposed a faster linear method for the estimation of the parameters of the phase portrait. However, their formulation leads to the minimization of a different error measure, which is not as robust as the nonlinear least-squares procedure in the presence of noise, and also makes the implicit assumption that the orientation field was truly generated by a phase portrait model (with an extra weighting factor to compensate for noise sensitivity). We propose a new derivation of Shu and Jain's linear estimator that leads to similar estimation equations, while making explicit the nature of the error measure. Our procedure includes an iterative scheme, of which Shu and Jain's linear estimator is a particular case. We show that our estimator is more robust to noise than Shu and Jain's linear estimator.
Architectural distortion is a subtle abnormality in mammograms, and a source of overlooking errors by radiologists. Computer-aided diagnosis (CAD) techniques can improve the performance of radiologists in detecting masses and calcifications; however, most CAD systems have not been designed to detect architectural distortion. We present a new method to detect and localize architectural distortion by analyzing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The orientation field is filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analyzed to produce three phase portrait maps: node, saddle, and spiral. The node map is linearly filtered, thresholded, and morphologically filtered to detect architectural distortion. The method was tested with 18 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 88% was obtained at 15 false positives per image. Several possibilities for the improvement of the technique are being explored. A qualitative analysis of the performance of the method with stellate lesions indicates potential for enhancement of the technique.