Architectural Distortion In Mammograms Research Articles

Detection of multiple abnormalities of breast cancer in mammograms using a deep dilated fully convolutional neural network

Breast cancer is one of the serious health issues which are quite frequent among women and the mortality rate due to it can be reduced if detected and diagnosed at the early stage. In this paper, a new generalized deep learning architecture, deep dilated fully convolutional neural network (DDFCNN), is designed to detect multiple abnormalities like mass, microcalcification, and architectural distortion in mammograms. The proposed DDFCNN architecture consists of a feature and a dilation module. In feature module, convolutional kernels of various sizes are used to capture information at different scales. Since there is no loss of resolution in the output of the dilation module, it renders more details which helps in localizing the anomaly properly. Several experiments are conducted on a widely used databases — DDSM and mini-MIAS where an accuracy of 95.33%, 91.42%, and 92.67% is measured for detection of mass, microcalcification, and architectural distortion, respectively with a FPs/I of 0.43, 0.50, and 0.46 for DDSM and similar assessment for mini-MIAS database are 96.56%, 89.63%, and 95.07% with a FPs/I of 0.29, 0.51, and 0.31. Moreover, the overall performance in detection of all the abnormalities in combination is 93.14% with 0.46 as FPs/I for DDSM whereas for mini-MIAS is 93.75% with 0.37 as FPs/I. The performance is further compared with the state-of-the-other methods where the proposed approach takes an edge.

Extreme learning machine based investigation on automated detection of architectural distortion in mammograms

Breast cancer, having its origin from the breast tissue is usually detected by mammographic screening. The early detection of breast cancer reduces the mortality rate. A subtle type of breast cancer that often leads to misinterpretation by radiologists is architectural distortion. Though the existing computer aided diagnosis systems efficiently and effectively detect the presence of micro-calcification and masses, the diagnosis of architectural distortion lacks a promising method. This project attempts to detect and classify the regions of mammograms having architectural distortion. MIAS and DDSM database images are enrolled in this research work. 350 region of interests (ROIs) of each architectural distortion and normal cases were extracted. They were subjected to a filtering process, followed by contrast enhancement. Application of Gabor filter to the images resulted in orientation differences between the normal and abnormal images. Statistical features extracted from the resulting images were classified using extreme learning machine classifier. The experimental results obtained from extreme learning machine in comparison with support vector machine had an accuracy of 98.49% and 87.21% for MIAS and DDSM respectively. The accuracy of combined database of which is 85.38%.

Extreme learning machine-based investigation on automated detection of architectural distortion in mammograms

Extreme learning machine-based investigation on automated detection of architectural distortion in mammograms

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.

Detection of architectural distortion in mammograms using geometrical properties of thinned edge structures

AbstractThe proposed method detects the most commonly missed breast cancer symptom, Architectural Distortion. The basis of the method lies in the analysis of geometrical properties of abnormal patterns that correspond to Architectural Distortion in mammograms. Pre-processing methods are employed for the elimination of Pectoral Muscle (PM) region from the mammogram and to localize possible centers of Architectural Distortion. Regions that are candidates to contain centroids of Architectural Distortion are identified using a modification of the isotropic SUSAN filter. Edge features are computed in these regions using Phase Congruency, which are thinned using Gradient Magnitude Maximization. From these thinned edges, relevant edge structures are retained based on three geometric properties namely eccentricity to retain near linear structures, perpendicular distance from each such structure to the centroid of the edges and quadrant support membership of these edge structures. Features for classification are g...

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.

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation.

Architecture distortion (AD) is an important and early sign of breast cancer, but due to its subtlety, it is often missed on the screening mammograms. The objective of this study is to create a quantitative approach for texture classification of AD based on various texture models, using support vector machine (SVM) classifier. The texture analysis has been done on the region of interest (ROI) selected from the original mammogram. A comprehensive analysis has been done on samples from three databases; out of which, two data sets are from the public domain, and the third data set is for clinical evaluation. The public domain databases are IRMA version of digital database for screening mammogram (DDSM) and Mammographic Image Analysis Society (MIAS). For clinical evaluation, the actual patient's database has been obtained from ACE Healthways, Diagnostic Centre Ludhiana, India. The significant finding of proposed study lies in appropriate selection of the size of ROIs. The experiments have been done on fixed size of ROIs as well as on the ground truth (variable size) ROIs. Best results pertain to an accuracy of 92.94% obtained in case of DDSM database for fixed-size ROIs. In case of MIAS database, an accuracy of 95.34% is achieved in AD versus non-AD (normal) cases for ground truth ROIs. Clinically, an accuracy of 88% was achieved for ACE dataset. The results obtained in the present study are encouraging, as optimal result has been achieved for the proposed study in comparison with other related work in the same area.

Architectural Distortion Detection in Mammogram using Contourlet Transform and Texture Features

Breast Cancer is one of the most affecting diseases in the world. Architectural Distortion is one of the indications of breast cancer. It is an abnormal arrangement of tissue strands of the breast, often a radial or perhaps a somewhat random pattern, but without any associated mass as the apparent cause of this distortion. This project used Contourlet Transform based method to detect the location of the architectural distortion in mammograms. The dominant angle is detected in this Contourlet decomposition which comprises of Laplacian Pyramidal Filter bank and Steerable Gaussian Filter. The textural features will not show any deviation in the distorted area. Here the methods proposed will assist the Radiologist to identify the unidentified potential sites of distortion. Therefore the main aim of this work becomes the reduction of the region of interests. In this project 39 cases were testes in which 19 are Architectural Distorted image from the MIAS database and 17 Normal images. The proposed algorithm detected 1502 regions of interest (ROIs) with 247 True Positives and 1255 False Positives. In this paper 26 textural features were considered and result is obtained as 0.77 with an artificial neural network. The proposed methods have significant result in detecting the architectural distortion in mammograms of interval cancer cases.

Measures of angular spread and entropy for the 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. 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. 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 0.80at 5.2 false positives (FPs) per patient. The proposed methods can detect architectural distortion in prior mammograms taken 15months (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.

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.

Analysis of Oriented Texture with Applications to the Detection of Architectural Distortion in Mammograms

Abstract The presence of oriented features in images often conveys important information about the scene or the objects contained; the analysis of oriented patterns is an important task in the general framework of image understanding. As in many other applications of computer vision, the general framework for the understanding of oriented features in images can be divided into low- and high-level analysis. In the context of the study of oriented features, low-level analysis includes the detection of oriented features in images; a measure of the local magnitude and orientation of oriented features over the entire region of analysis in the image is called the orientation field. High-level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model. This book presents an analysis of several important methods for the detection of oriented features in images, and a discussion of the phase portrait metho...

Advances in the diagnostic technogues of architectural distortion in mammograms

乳腺癌是全球女性发病率最高的恶性肿瘤,通过筛查实现早期发现、早期诊断和早期治疗对降低乳腺癌死亡率至关重要.乳腺钼靶X线摄影术是目前最普遍适用的乳腺癌筛查方法.有效检测乳腺钼靶X线影像中的结构扭曲病灶有利于提高筛查的质量和效率.就目前乳腺钼靶X线影像中结构扭曲检测技术的研究现状、存在的问题和发展趋势进行了综述。

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

Objective One of the commonly missed signs of breast cancer is architectural distortion. We have developed techniques for the detection of architectural distortion in mammograms, based on the analysis of oriented texture through the application of Gabor filters and a linear phase portrait model. In this paper, we propose constraining the shape of the general phase portrait model as a means to reduce the false-positive rate in the detection of architectural distortion.

Gabor filters and phase portraits for the detection of architectural distortion in mammograms

Gabor filters and phase portraits for the detection of architectural distortion in mammograms

Gabor filters and phase portraits for the detection of architectural distortion in mammograms

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 localise architectural distortion by analysing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The curvilinear structures (CLS) of interest (spicules and fibrous tissue) are separated from confounding structures (pectoral muscle edge, parenchymal tissue edges, breast boundary, and noise). The selected core CLS pixels and the orientation field are filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analysed to produce three phase portrait maps: node, saddle, and spiral. The node map is further analysed in order to detect the sites of architectural distortion. The method was tested with 19 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 84% was obtained at 7.8 false positives per image.

Characterization of architectural distortion in mammograms

The analysis of mammograms is a difficult task due to the subtle appearance of some lesions. Computer-aided diagnosis (CAD) have been shown to improve the sensitivity of detection of masses and calcifications; however, there is a need for dedicated methods to detect architectural distortion in the absence of a central mass. Improvement in the detection of architectural distortion may be expected to result in better prognosis for patients with early stages of breast cancer. By employing the concept of phase portraits, a method to characterize architectural distortion in mammograms using texture orientation fields is presented. The results obtained show that the proposed technique can achieve good discrimination between architectural distortion and other parenchymal patterns. Such a technique would be applicable, in a CAD system, to images that have cleared the stages of detection of calcifications and masses with no positive findings.