Architectural distortion is an important sign of early breast cancer. Due to its subtlety, it is often missed during screening. We propose a method to detect architectural distortion in prior mammograms of interval-cancer cases based on statistical measures of oriented patterns. Oriented patterns were analyzed in the present work because regions with architectural distortion contain a large number of tissue structures spread over a wide angular range. Two new types of cooccurrence matrices were derived to estimate the joint occurrence of the angles of oriented structures. Statistical features were computed from each of the angle cooccurrence matrices to discriminate sites of architectural distortion from falsely detected regions in normal parts of mammograms. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases with the application of Gabor filters and phase portrait analysis. For each ROI, Haralick's 14 features were computed using the angle cooccurrence matrices. The best result obtained in terms of the area under the receiver operating characteristic (ROC) curve with the leave-one-patient-out method was 0.76; the free-response ROC curve indicated a sensitivity of 80% at 4.2 false positives per patient.
We present a method using statistical measures of the orientation of texture to characterize and detect architectural
distortion in prior mammograms of interval-cancer cases. Based on the orientation field, obtained by
the application of a bank of Gabor filters to mammographic images, two types of co-occurrence matrices were
derived to estimate the joint occurrence of the angles of oriented structures. For each of the matrices, Haralick's
14 texture features were computed. From a total of 106 prior mammograms of 56 interval-cancer cases and
52 mammograms of 13 normal cases, 4,224 regions of interest (ROIs) were automatically obtained by applying
Gabor filters and phase portrait analysis. For each ROI, statistical features were computed using the angle
co-occurrence matrices. The performance of the features in the detection of architectural distortion was analyzed
and compared with that of Haralick's features computed using the gray-level co-occurrence matrices of
the ROIs. Using logistic regression for feature selection, an artificial neural network for classification, and the
leave-one-image-out approach for cross-validation, the best result achieved was 0.77 in terms of the area under
the receiver operating characteristic (ROC) curve. Analysis of the free-response ROC curve yielded a sensitivity
of 80% at 5.4 false positives per image.
We present methods for the detection of architectural distortion in mammograms of interval-cancer cases taken
prior to the diagnosis of breast cancer using measures of angular distribution derived from Gabor filter responses
in magnitude and angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum.
A total of 4224 regions of interest (ROIs) were automatically obtained using Gabor filters and phase portrait
analysis from 106 prior mammograms of 56 interval-cancer cases with 301 ROIs related to architectural distortion,
and from 52 mammograms of 13 normal cases. Images of coherence and orientation strength were derived from
the Gabor responses in magnitude and orientation. Each ROI was represented by the entropy of the angular
histogram composed with the Gabor magnitude response, angle, coherence, and orientation strength; the entropy
of the angular spread of power in the Fourier spectrum was also computed. Using stepwise logistic regression
for feature selection and the leave-one-image-out method in feature selection and pattern classification, the area
under the receiver operating characteristic curve of 0.76 was obtained with an artificial neural network based on
radial basis functions. Analysis of the free-response receiver operating characteristics indicated 82% sensitivity
at 7.2 false positives per image.
This paper presents methods for the detection of architectural distortion in mammograms of interval-cancer cases
taken prior to the diagnosis of breast cancer, using Gabor filters, phase portrait analysis, fractal dimension (FD),
and analysis of the angular spread of power in the Fourier spectrum. In the estimation of FD using the Fourier
power spectrum, only the distribution of power over radial frequency is considered; the information regarding
the angular spread of power is ignored. In this study, the angular spread of power in the Fourier spectrum is
used to generate features for the detection of spiculated patterns related to architectural distortion. Using Gabor
filters and phase portrait analysis, a total of 4224 regions of interest (ROIs) were automatically obtained from
106 prior mammograms of 56 interval-cancer cases, including 301 ROIs related to architectural distortion, and
from 52 mammograms of 13 normal cases. For each ROI, the FD and measures of the angular spread of power
were computed. Feature selection was performed using stepwise logistic regression. The best result achieved,
in terms of the area under the receiver operating characteristic curve, is 0.75 ± 0.02 with an artificial neural
network including radial basis functions. Analysis of the performance of the methods with free-response receiver
operating characteristics indicated a sensitivity of 0.82 at 7.7 false positives per image.
Identification, localization, and segmentation of the thoracic, abdominal, and pelvic organs are important steps in computer-aided diagnosis, treatment planning, landmarking, and content-based retrieval of biomedical images. In this context, to aid the identification of the lower abdominal organs, to assist in image-guided surgery or treatment planning, to separate the abdominal cavity from the lower pelvic region, and to improve the process of localization of abdominal pathology, we propose methods to identify and segment automatically the pelvic girdle in pediatric computed tomographic (CT) images. The opening-by-reconstruction procedure was used for segmentation of the pelvic girdle. The methods include procedures to represent the pelvic surface by a quadratic model using linear least-squares estimation and to refine the model using deformable contours. The result of segmentation of the pelvic girdle was assessed quantitatively and qualitatively by comparing with the segmentation performed independently by a radiologist. On the basis of quantitative analysis with 13 CT exams of six patients, including a total of 277 slices with the pelvis, the average Hausdorff distance was determined to be 5.95 mm, and the average mean distance to the closest point (MDCP) was 0.53 mm. The average MDCP is comparable to the size of one pixel, on the average.
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