Detection of Architectural Distortion in Prior Mammograms <em>via</em> Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of ...

University of Calgary 

Segmentation of the primary tumor mass in neuroblastoma could aid radiologists by facilitating reproducible and objective quantification of the tumor's tissue composition and size. However, due to the heterogeneous nature of the tissue components of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, some of which possess strong similarities with adjacent nontumoral tissues in computed tomographic (CT) images, segmentation of the tumor is a difficult problem. In this context, landmarking methods are proposed to assist in the segmentation of neuroblastic tumors. Methods are proposed to identify and segment automatically the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle. The use of the landmarks assisted in limiting the scope of the tumor segmentation process to the abdomen, and resulted in the reduction of the false-positive error rates by 26.9%, on the average, over 10 CT exams, and improved the result of segmentation of neuroblastic tumors.

Landmarking of computed tomographic images to assist in segmentation of abdominal tumors caused by neuroblastoma.

We propose methods to perform automatic identification of the rib structure, the vertebral column, and the spinal canal in computed tomographic (CT) images of pediatric patients. The segmentation processes for the rib structure and the vertebral column are initiated using multilevel thresholding and the results are refined using morphological image processing techniques with features based on radiological and anatomical prior knowledge. The Hough transform for the detection of circles is applied to a cropped edge map that includes the thoracic vertebral structure. The centers of the detected circles are used to derive the information required for the opening-by-reconstruction algorithm used to segment the spinal canal. The methods were tested on 39 CT exams of 13 patients; the results of segmentation of the vertebral column and the spinal canal were assessed quantitatively and qualitatively by comparing with segmentation performed independently by a radiologist. Using 13 CT exams of six patients, including a total of 458 slices with the vertebra from different sections of the vertebral column, the average Hausdorff distance was determined to be 3.2 mm with a standard deviation (SD) of 2.4 mm; the average mean distance to the closest point (MDCP) was 0.7 mm with SD = 0.6 mm. Quantitative analysis was also performed for the segmented spinal canal with three CT exams of three patients, including 21 slices with the spinal canal from different sections of the vertebral column; the average Hausdorff distance was 1.6 mm with SD = 0.5 mm, and the average MDCP was 0.6 mm with SD = 0.1 mm.

Automatic segmentation of the ribs, the vertebral column, and the spinal canal in pediatric computed tomographic images.

Architectural distortion is a commonly missed sign of breast cancer. This paper investigates 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, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval-cancer and also normal cases. A total of 4212 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 262 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick's texture features were computed. Feature selection was performed using stepwise logistic regression and in terms of the area under the receiver operating characteristics (ROC) curve (AUC). The best results achieved, in terms of AUC, are 0.75 with the Bayesian classifier, 0.71 with Fisher linear discriminant analysis, and 0.76 with an artificial neural network (ANN) based on radial basis functions (RBF). Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 10.5 false positives per image.

Detection of architectural distortion in prior mammograms of interval-cancer cases with neural networks.

Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis, treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors is difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and volume. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, we explore methods for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images.

Landmarking and segmentation of computed tomographic images of pediatric patients with neuroblastoma.

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 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 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick's texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.

Computer-aided detection of architectural distortion in prior mammograms of interval cancer.

Correction to "Detection of architectural distortion in prior mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis [1]".

We present methods for the detection of sites of architectural distortion in prior mammograms of interval-cancer cases. We hypothesize that screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. The methods are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase portrait analysis, 4224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' measures, and Haralick's 14 features were computed. The areas under the receiver operating characteristic curves obtained using the features selected by stepwise logistic regression and the leave-one-ROI-out method are 0.76 with the Bayesian classifier, 0.75 with Fisher linear discriminant analysis, and 0.78 with a single-layer feed-forward neural network. Free-response receiver operating characteristics indicated sensitivities of 0.80 and 0.90 at 5.8 and 8.1 false positives per image, respectively, with the Bayesian classifier and the leave-one-image-out method.

Detection of architectural distortion in prior mammograms.

Architectural distortion is an important sign of early breast cancer. We present methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions.

Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms.

We propose a method for the detection of architectural distortion in prior mammograms of interval-cancer cases based on the expected orientation of breast tissue patterns in mammograms.

Shantanu Banik

Department of Electrical and Computer Engineering,
Schulich School of Engineering,
Department of Electrical and Computer Engineering, Schulich School of Engineering

University of Calgary

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Shantanu Banik

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Electrical and Computer Engineering,Schulich School of Engineering,Department of Electrical and Computer Engineering, Schulich School of Engineering

University of Calgary

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Department of Electrical and Computer Engineering,
Schulich School of Engineering,
Department of Electrical and Computer Engineering, Schulich School of Engineering