Classification Of Microcalcifications Research Articles

Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

Computational growth model of breast microcalcification clusters in simulated mammographic environments

BackgroundWhen screening for breast cancer, the radiological interpretation of mammograms is a difficult task, particularly when classifying precancerous growth such as microcalcifications (MCs). Biophysical modeling of benign vs. malignant growth of MCs in simulated mammographic backgrounds may improve characterization of these structures MethodsA mathematical model based on crystal growth rules for calcium oxide (benign) and hydroxyapatite (malignant) was used in conjunction with simulated mammographic backgrounds, which were generated by fractional Brownian motion of varying roughness and quantified by the Hurst exponent to mimic tissue of varying density. Simulated MC clusters were compared by fractal dimension, average circularity of individual MCs, average number of MCs per cluster, and average cluster area. ResultsBenign and malignant clusters were distinguishable by average circularity, average number of MCs per cluster, and average cluster area with p<0.01 across all Hurst exponent values considered. Clusters were distinguishable by fractal dimension with p<0.05 in low Hurst exponent environments. As the Hurst exponent increased (tissue density increased) benign and malignant MCs became indistinguishable by fractal dimension. ConclusionsThe fractal dimension of MCs changes with breast tissue density, which suggests tissue environment plays a role in regulating MC growth. Benign and malignant MCs are distinguishable in all types of tissue by shape, size, and area, which is consistent with findings in the literature. These results may help to better understand the effects of the tissue environment on tumor progression, and improve classification of MCs in mammograms via computer-aided diagnosis.

A Survey of Computer-aided Detection of Breast Cancer with Mammography

Computer-aided detection (CAD) systems can be served as a second view for radiologist. An overview of recent development in CAD methods are presented in this paper. Abnormalities detection, abnormalities classification and content-based image retrieval (CBIR) are briefly reviewed. For the abnormalities detection, micro-calcification detection, mass detection and multi-view based detection are introduced. For the abnormalities classification, micro-calcification classification and mass classification are given.

Robust automatic classification of benign and malignant microcalcification and mass in digital mammography

Breast cancer is the most dangerous cancer among women and second mortality among them. Mammography is the efficient methodology used in early finding of breast cancer. However, mammograms requires high amount of skill and there is a possibility of radiologist to misunderstand it. Hence, computer aided diagnosis are used for finding the abnormalities in mammograms. Automated classification of mass and microcalcification system is proposed in this work using NSCT and SVM. The classification of abnormalities is achieved by extracting the microcalcification and mass features from the contourlet coefficients of the image and the results are used as an input to the SVM. The proposed automated system classifies the mammogram as normal or abnormal and result is abnormal, then it classifies the abnormal severity as benign or malignant. The evaluation of the proposed system is conceded on MIAS database. The experimentation result shows that the proposed system contributes improved classification rate.

Robust Automatic Classification of Benign and Malignant Microcalcification and Mass in Digital Mammography

Robust Automatic Classification of Benign and Malignant Microcalcification and Mass in Digital Mammography

Multi-View Probabilistic Classification of Breast Microcalcifications.

Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. In this paper we apply a multi-view-classifier for the task. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single benign or malignant decision. The proposed method was evaluated on a large number of cases from a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the advantage of the proposed multi-view classification algorithm that automatically learns the best way to combine the views.

Classification of Clustered Microcalcifications using MLFFBP-ANN and SVM

The classifier is the last phase of Computer-Aided Diagnosis (CAD) system that is aimed at classifying Clustered Microcalcifications (MCCs). Classifier classifies MCCs into two classes. One class is benign and other is malignant. This classification is done based on some meaningful features that are extracted from enhanced mammogram. A number of classifiers have been proposed for CAD system to classify MCCs as benign or malignant. Recently, researchers have used Artificial Neural Networks (ANNs) as classifiers for many applications. Multilayer Feed-Forward Backpropagation (MLFFB) is the most important ANN that has been successfully used by researchers to solve various problems. Similarly, Support Vector Machines (SVMs) belong to another category of classifiers that researchers have recently given considerable attention. So, to explore MLFFB and SVM classifiers for MCCs classification problem, in this paper, Levenberg-Marquardt Multilayer Feed-Forward Backpropagation ANN (LM-MLFFB-ANN) and Sequential Minimal Optimization (SMO) based SVM (SMO-SVM) are used for the classification of MCCs. Thus, a comparative evaluation of the relative performance of LM-MLFFBP-ANN and SMO-SVM is investigated to classify MCCs as benign or malignant. For this comparative evaluation, first suitable features are extracted from mammogram images of DDSM database. After this, suitable features are selected using Particle Swarm Optimization (PSO). At the end, MCCs are classified using LM-MLFFBP-ANN and SMO-SVM classifiers based on the selected features. Confusion matrix and ROC analysis are used to measure the performance of LM-MLFFBP-ANN and SMO-SVM classifiers. Experimental results indicate that the performance of SMO-SVM is better than that of LM-MLFFBP-ANN.

Orthogonal Features based Classification of Microcalcification in Mammogram using Jacobi Moments

Objective: Breast calcifications can be present in mammograms which are one of the most important risk indicators of breast cancer. Digital mammography is a reliable tool to detect breast cancer at the early stage with no symptoms. The objective of this research work is to classify the microcalcification patterns into benign and malignant. Methods: In this research, a novel approach is proposed for classification of microcalcifications based on shift-invariant transform, Jacobi moments and Support Vector Machines (SVM). The Jacobi moments are used to extract orthogonal features from the location of microcalcifications based on orthogonal or weighted polynomial basis which uses recurrence relation to avoid the loss of precisions. The Jacobi features will be given as input to SVM classifier for classifying the mammogram images into normal and abnormal. The abnormality will be further classified into benign or malignant. Findings: The validity of the proposed approach is evaluated using MIAS database. In the process of mammogram enhancement, the experimental results shows that shift-invariant transform achieves better results than contourlet transform. The classification rate of proposed system is 98.65% for normal, 95.80% for benign and 93.35% for malignant accuracy. The performance of the proposed approaches measured by sensitivity, specificity and confusion matrix. The measurement of performance is 0.84 sensitivity and 0.95 specificity for stage1 and 0.85 sensitivity and 0.83 specificity for stage 2. Application/Improvement: The results show that our proposed approach gives high level of accuracy for classification of microcalcification. This approach is very useful to avoid unnecessary biopsy.

Microcalcification Classification in Digital Mammogram using Moment based Statistical Texture Feature Extraction and SVM

Microcalcification Classification in Digital Mammogram using Moment based Statistical Texture Feature Extraction and SVM

Ensemble features selection method as tool for breast cancer classification

This work aims to gather experimental evidence of features relevance, as well as finding a breast cancer classification scheme that provides the high performance over the area under receiver operating characteristic curve (AUC). An ensemble feature selection method (named RMean) based on the mean criteria for indexing relevant features is presented. The proposed method provided better classification performances (statistically significant) than those who constitute the baseline, attaining AUC scores of 0.7775 with the support vector machine on microcalcifications dataset and 0.9440 with the feed-forward-backpropagation neural network classifier on masses dataset. The most relevant features for microcalcifications classification were: mammographic stroma distortion, density, right bottom quadrant, perimeter, standard deviation, entropy, and angular second moment. Meanwhile, to classify masses were: mammographic stroma distortion, mammographic calcification, mammographic nodules, density, circularity, roughness, and shape.

Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms

In this paper we present an evaluation of four different algorithms based on Mathematical Morphology, to detect the occurrence of individual micro-calcifications in digitized mammogram images from the mini-MIAS database. A morphological algorithm based on contrast enhancement operator followed by extended maxima thresholding retrieved most of micro-calcifications. In order to reduce the number of false positives produced in that stage, a set of features in the spatial, texture and spectral domains was extracted and used as input in a support vector machine (SVM). Results provided by TMVA (Toolkit for Multivariate Analysis) produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. An additional parameter, that we called Signal Efficiency*Purity (denoted SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise. The SVM with Gaussian kernel was the most suitable for detecting micro-calcifications. Sensitivity was obtained for the three types of breast. For glandular, it detected 137 of 163 (84.0%); for dense tissue, it detected 74 of 85 (87.1%) and for fatty breast, it detected 63 of 71 (88.7%). The overall sensitivity was 85.9%. The system also was tested in normal images, producing an average of false positives per image of 13 in glandular tissue, 11 in dense tissue and 15 in fatty tissue.

Non-invasive classification of microcalcifications with phase-contrast X-ray mammography.

Microcalcifications can be indicative in the diagnosis of early breast cancer. Here we report a non-invasive diagnostic method that may potentially distinguish between different types of microcalcifications using X-ray phase-contrast imaging. Our approach exploits the complementary nature of the absorption and small-angle scattering signals of microcalcifications, obtained simultaneously with an X-ray grating interferometer on a conventional X-ray tube. We demonstrate that the new approach has 100% sensitivity and specificity when applied to phantom data, and we provide evidence of the solidity of the technique by showing its discrimination power when applied to fixed biopsies, to non-fixed tissue specimens and to fresh, whole-breast samples. The proposed method might be further developed to improve early breast cancer diagnosis and has the potential to increase the diagnostic accuracy and reduce the number of uncomfortable breast biopsies, or, in case of widespread microcalcifications, to select the biopsy site before intervention.

Automatic Detection and Classification of Microcalcification on Mammographic Images

Mammography is a best technique for early detection of breast cancer. This paper deals with the detection of micro calcification on mammographic images. It appears as white speckles in images. Micro calcification is a tiny bit, which may not be clearly visible because of low contrast in the image. Our goal is to apply image processing technique to enhance the images and subsequently extract the features from the suspicious region with great accuracy and reliability to detect whether micro calcification is malignant or not. Keywords: Micro calcification, malignant tumor

A fast and simple method for the visual enhancement of microcalcifications in digital mammograms based on mathematical morphology

A fast and simple method for the visual enhancement of small bright details in digital mam- mograms based on mathematical morphology is proposed. By a proper choice of the shape and size of the structuring element, an algorithm for a particular processing task - in this case, for the visual enhancement of microcalcifications in digital mammograms - was designed. The efficiency of the proposed algorithm was tested on publicly available mammograms from the mammographic image analysis society database. In all tested cases (23 mammograms), the proposed method successfully segmented and enhanced the existing microcalcifications, in- dependently verified by medical experts. The proposed procedure may be used both as a visual aid in clinical mammogram analysis or as a preprocessing step for further processing, such as segmentation, classification and detection of microcalcifications. Moreover, the algorithm is very fast and robust, thus applicable to real-time mammogram processing.

Impact of Magnification Views on the Characterization of Microcalcifications in Digital Mammography

To evaluate the additional benefit of true geometric (air-gap) magnification views for the characterization of microcalcifications in digital mammography. After ethical approval, we retrospectively reviewed patient records to identify 100 patients with suspicious microcalcifications (35 malignant, 65 benign) who had a standard digital mammography and an additional digital magnification view in the same projection within three months. All images were obtained using an amorphous silicon-based full-field digital system (Senographe 2000 D, GE Healthcare, Chalfont St. Giles, UK). Images were independently analyzed by six board-certified radiologists. The probability of malignancy was estimated using first standard contact mammography alone (MG) and then mammography in combination with the magnification view (MG+MAG) using a modified Breast Imaging Reporting and Data System (BI-RADS) classification system and a percentage scale. Results were compared using receiver operating characteristic (ROC) analysis. In addition, readers assessed the subjective visibility of the calcifications. For all six readers combined, the area under the curve (AUC) was 0.664 ± 0.052 for MG and 0.813 ± 0.042 for MG + MAG, resulting in a statistically significant improvement of 0.148 ± 0.120. Each reader had a higher AUC for MG + MAG than MG, with the improvement being statistically significant in four of the six readers. In 76.34 % of the cases, MG + MAG resulted in better visibility of calcifications compared with mammography alone. In 33 % slightly more and in 39 % significantly more calcifications were found. Even in digital mammography with the option of using electronic magnification (zoom) at the viewing workstation, true geometric (air-gap) magnification views remain important for the visibility and correct classification of microcalcifications and for the assessment of their extent.

Weaver Ant Colony Optimization-Based Neural Network Learning for Mammogram Classification

Neural Networks (NNs) have been efficaciously used for classification purposes in medical domains, including the classification of microcalcifications in digital mammograms. Unfortunately, for a NN to be effective in a particular purview, its architecture, training algorithm and the domain variables selected as inputs must be amply chosen. In this paper, a novel Ant Colony Optimization (ACO) based learning approach with a modified architecture is proposed to speed up the learning phase of a Backpropagation Neural Network (BPN) classifier. The novel ACO simulates the behavior of weaver ants, known for their unique nest building behavior where workers construct nests by weaving together leaves using larval silk. The proposed Weaver Ant Colony Optimization (WACO) based Backpropagation Neural Network (WACO-BPN) is applied for classifying digital mammograms received from MIAS database. The performance is analyzed with Receiver Operating Characteristics (ROC) curve. The greater accuracy of 97% states the grander performance of the proposed neural network learning approach.

A ROBUST APPROACH TO CLASSIFY MICROCALCIFICATION IN DIGITAL MAMMOGRAMS USING CONTOURLET TRANSFORM AND SUPPORT VECTOR MACHINE

Mammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by extracting the microcalcification features from the Contourlet coefficients of the image and the outcomes are used as an input to the SVM for classification. The system classifies the mammogram images as normal or abnormal and the abnormal severity as benign or malignant. The evaluation of the system is carried on using Mammography Image Analysis Society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate.

Development of a CAD System for Automatic Classification of Microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michèle Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis.

Abstract P4-03-06: Non-invasive classification of microcalcifications by the use of X-ray phase contrast mammography as a novel tool in breast diagnostics

Abstract Purpose: Phase-contrast and scattering-based x-ray imaging are known to provide additional and complementary information to conventional, absorption-based methods. The goal of this study is to evaluate this method to distinguish between benign and malignant microcalcifications with the benefit to increase accuracy of early breast cancer diagnosis. Phase-contrast mammography has been shown to increase image quality of native breast samples when compared to conventional mammography. Two major types of microcalcifications are found within breast tissue. Type I consist of calcium oxalate dehydrate and type II microcalcifications are composed of calcium phosphates. Type I is seen most frequently in benign lesions whereas type II is indicative for proliferative lesions, including carcinomas. Phase contrast mammography is shown here to distinguish between the two types of microcalcifications and therefore indicates a step forward to improve early breast cancer diagnosis. Material and Methods: Freshly dissected breast specimens (n = 50) were imaged using a Talbot-Lau interferometer equipped with a conventional x-ray tube; the interferometer was operated at the fifth Talbot distance, tube voltage of 40 kVp with mean energy of 28 keV, and current of 25 mA. The device simultaneously recorded absorption, differential phase and small-angle scattering signals. These quantities were combined into novel, high-frequency-enhanced radiographic images. Histopathological analysis was performed and regions of interests correlated with the findings of phase contrast mammography. Results: Our novel imaging approach yields complementary and otherwise inaccessible information on electron density distribution and small-angle scattering power of the sample at microscopic scale. Recently we generated the world's first phase contrast mammograms of native, not-fixed human whole breast samples. Our results indicate the superiority of the new technique with respect to image quality and lesion conspicuity. A clinical reader study is currently carried out. Exploiting the multiple, complementary information obtained by grating-based interferometry, we are able to classify microcalcifications by a non-invasive technique within the clinical environment. By considering the small-angle scattering signal as a complement to the absorption signal, our method can analyze the differences in the attenuation coefficient as well as in the crystal structure of the microcalcifications. Further, the scattering signal is used to decouple the thickness parameter. We demonstrate that type I and type II microcalcifications give opposite absorption and scattering signals and in addition the small-angle scattering signal helps to determine the type of microcalcification. Conclusions: The potential clinical significance of phase-contrast enhanced mammography has been evaluated by our team. This technique yields improved diagnostic capabilities when compared with conventional mammography, can provide superior contrast, inaccessible and complementary information, and potentially also reduce dose deposition. The non-invasive classification of microcalcifications is an important step toward early diagnosis and differentiation of breast lesions. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-03-06.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

BackgroundDigital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading.MethodsFirstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system.ResultsThe results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85.ConclusionsCompared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.