Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model

Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field

Objective. In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT.Approach. Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases.Main results. A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement (p< 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement.Significance. The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening.

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

A computer-aided detection of the architectural distortion in digital mammograms using the fractal dimension measurements of BEMD

Achieving a high performance for the detection and characterization of architectural distortion in screening mammograms is important for an efficient breast cancer early detection. Viewing a mammogram image as a rough surface that can be described using the fractal theory is a well-recognized approach. This paper presents a new fractal-based computer-aided detection (CAD) algorithm for characterizing various breast tissues in screening mammograms with a particular focus on distinguishing between architectural distortion and normal breast parenchyma. The proposed approach is based on two underlying assumptions: (i) monitoring the variation pattern of fractal dimension, with the changes of the image resolution, is a useful tool to distinguish textural patterns of breast tissue, (ii) the bidimensional empirical mode decomposition (BEMD) algorithm appropriately generates a multiresolution representation of the mammogram. The proposed CAD has been tested using different validation datasets of mammographic regions of interest (ROIs) extracted from the Digital Database for Screening Mammography (DDSM) database. The validation ROI datasets contain architectural distortion (AD), normal breast tissue, and AD surrounding tissue. The highest classification performance, in terms of area under the receiver operating characteristic curve, of Az = 0.95 was achieved when the proposed approach applied to distinguish 187 architectural distortion depicting regions from 2191 normal breast parenchyma regions. The obtained results validate the underlying hypothesis and demonstrate that effectiveness of capturing the variation of the fractal dimension measurements within an appropriate multiscale representation of the digital mammogram. Results also reveal that this tool has the potential of prescreening other key and common mammographic signs of early breast cancer.

Characterizing Architectural Distortion in Mammograms by Linear Saliency

Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (A _z ) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.

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

PURPOSE

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.

METHODS

Potential sites of architectural distortion were detected using node maps obtained through the application of a bank of Gabor filters and linear phase portrait modeling. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by three types of entropy measures of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum, including Shannon's entropy, Tsallis entropy for nonextensive systems, and Rényi entropy for extensive systems.

RESULTS

Using the entropy measures with stepwise logistic regression and the leave-one-patient-out method for feature selection and cross-validation, an artificial neural network resulted in an area under the receiver operating characteristic curve of 0.75. Free-response receiver operating characteristics indicated a sensitivity of 0.80 at 5.2 false positives (FPs) per patient.

CONCLUSION

The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, with a high sensitivity and a moderate number of FPs per patient. The results are promising and may be improved with additional features to characterize subtle abnormalities and larger databases including prior mammograms.

Recognizing architectural distortion in mammogram: a multiscale texture modeling approach with GMM

We propose a generative model for constructing an efficient set of distinctive textures for recognizing architectural distortion in digital mammograms. In the first layer of the proposed two-layer architecture, the mammogram is analyzed by a multiscale oriented filter bank to form texture descriptor of vectorized filter responses. Our model presumes that every mammogram can be characterized by a "bag of primitive texture patterns" and the set of textural primitives (or textons) is represented by a mixture of Gaussians which builds up the second layer of the proposed model. The observed textural descriptor in the first layer is assumed to be a stochastic realization of one (hard mapping) or more (soft mapping) textural primitive(s) from the second layer. The results obtained on two publicly available datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM), demonstrate the efficacy of the proposed approach.

Detection of architectural distortion in prior mammograms

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.

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

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.

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

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 the optic nerve head in fundus images of the retina with Gabor filters and phase portrait analysis

We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.

A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows

OBJECTIVE

We present herein a novel algorithm for architectural distortion detection that utilizes the point convergence index with the likelihood of lines (e.g., spiculations) relating to architectural distortion.

MATERIALS AND METHODS

Validation was performed using 25 computed radiography (CR) mammograms, each of which has an architectural distortion with radiating spiculations. The proposed method comprises five steps. First, the lines were extracted on mammograms, such as spiculations of architectural distortion as well as lines in the mammary gland. Second, the likelihood of spiculation for each extracted line was calculated. In the third step, point convergence index weighted by this likelihood was evaluated at each pixel to enhance distortion only. Fourth, local maxima of the index were extracted as candidates for the distortion, then classified based on nine features in the last step.

RESULTS

Point convergence index without the proposed likelihood generated 84.48/image false-positives (FPs) on average. Conversely, the proposed index succeeded in decreasing this number to 12.48/image on average when sensitivity was 100%. After the classification step, number of FPs was reduced to 0.80/image with 80.0% sensitivity.

CONCLUSION

Combination of the likelihood of lines with point convergence index is effective in extracting architectural distortion with radiating spiculations.