Classification Of Microcalcification Clusters Research Articles

Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network.

Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women’s health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications comes to be a key breakthrough in controlling breast cancer. In this study, the discriminant model based on image convolution is used to learn the image features related to the classification of clustered microcalcifications, and the graph convolutional network (GCN) based on topological graph is used to learn the spatial distribution characteristics of clustered microcalcifications. These two models are fused to obtain a complementary model of image information and spatial information. The results show that the performance of the fusion model proposed in this paper is obviously superior to that of the two classification models in the classification of clustered microcalcification.

Classification of clustered microcalcifications using different variants of backpropagation training algorithms

Classification of clustered microcalcifications using different variants of backpropagation training algorithms

Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network

BackgroundThe classification of benign and malignant microcalcification clusters (MCs) is an important task for computer-aided diagnosis (CAD) of digital breast tomosynthesis (DBT) images. Influenced by imaging method, DBT has the characteristic of anisotropic resolution, in which the resolution of intra-slice and inter-slice is quite different. In addition, the sharpness of MCs in different slices of DBT is quite different, among which the clearest slice is called focus slice. These characteristics limit the performance of CAD algorithms based on standard 3D convolution neural network (CNN).MethodsTo make full use of the characteristics of the DBT, we proposed a new ensemble CNN, which consists of the 2D ResNet34 and the anisotropic 3D ResNet to extract the 2D focus slice features and 3D contextual features of MCs, respectively. Moreover, the anisotropic 3D convolution is used to build 3D ResNet to avoid the influence of DBT anisotropy.ResultsThe proposed method was evaluated on 495 MCs in DBT images of 275 patients, which are collected from our collaborative hospital. The area under the curve (AUC) of receiver operating characteristic (ROC) and accuracy of classifying benign and malignant MCs using decision-level ensemble strategy were 0.8837 and 82.00%, which were significantly higher than the experimental results of 2D ResNet34 (AUC: 0.8264, ACC: 76.00%) and anisotropic 3D ResNet (AUC: 0.8455, ACC: 76.00%). Compared with the results of 3D features classification in the radiomics, the AUC of the deep learning method with decision-level ensemble strategy was improved by 0.0435, and the F1 score was improved from 79.37 to 85.71%. More importantly, the sensitivity increased from 78.13 to 84.38%, and the specificity increased from 66.67 to 77.78%, which effectively reduced the false positives of diagnosisConclusionThe results fully prove that the ensemble CNN can effectively integrate 2D features and 3D features, improve the classification performance of benign and malignant MCs in DBT, and reduce the false positives.

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A value equal to .

70. Computer aided detection system for the automated classification of clustered microcalcifications in digital mammograms

Purpose Mammography is the most effective and low-cost method for the early detection of breast cancers. The aim of this study was to develop an automated model in order to improve detection and characterization of clustered microcalcifications on full-field digital mammograms (FFDM). Methods We analyzed 98 digital mammograms extracted from the public database Breast Cancer Digital Repository with 276 microcalcification clusters; these images were previously evaluated in double blind by two radiologists which had identified the microcalcification clusters. We identified some regions of interest (ROI) on mammograms after pre-processing by using the Circular Hough Transform algorithm in order to recognize circles corresponding to the microcalcifications. Then, we selected morphological and morphometric features for each ROI, such as Speeded Up Robust Feature, as well as classic statistical features based on Haar Transform. These features were used for giving instructions to a Random Forest classifier in order to recognize – riga clustered microcalcifications normal and abnormal ROIs. Finally, we tested the performance of – riga this automated model in cross-validation and with statistical measurements. Results Statistical results ( Table 1 ) showed good performance of proposed model in automatic detection of breast clustered microcalcifications on FFDM (sensitivity over 90% with 3.54 false positives – riga per image), even in young patients with dense breast tissue (sensitivity over 88%) and according to published data [1] . Also classification performance of clustered microcalcifications (AUC 98,6 ± 0,1, Accuracy 97,6 ± 0,2%, Sensitivity 97,7 ± 0,4%, Specificity 96,2 ± 0,4%) was comparable with the state of the art [2] . Conclusions The proposed automated model enables to improve detection of clustered microcalcifications that could be characterized by difficult diagnostic interpretation especially when occurred in dense breast parenchyma. The proposed model results highly performing and comparable to the state-of-art approaches.

Automated Detection and Classification of Microcalcification Clusters with Enhanced Preprocessing and Fractal Analysis

This paper addresses the automated detection of microcalcification clusters from mammogram images by enhanced preprocessing operations on digital mammograms for automated extraction of breast tissue from background, removing artefacts occurring during image registration using X-rays, followed by fractal analysis of suspicious regions. Identification of breast of either left or right and realigning them to a standard position forms a primitive step in preprocessing of mammograms. As the next step in the process, pectoral muscles are separated. Suspicious regions of microcalcifications are identified and are subjected to further analysis of classifying it as benign or malignant. Texture features are representative of its malignancy and fractal analysis was carried out on extracted suspicious regions for its texture features. Principal Component Analysis was carried out to extract optimal features. Ten features were found to be an optimal number of reduced texture features without compromising on classification accuracy. Scaled conjugate Gradient Back propagation network was used for classification using reduced texture features obtained from PCA analysis. By varying hidden layer neurons, accuracy of results achieved by proposed methods is analysed and is calculated to reach maximum accuracy with an optimal level of 15 neurons. Accuracy of 96.3% was achieved with 10 fractal features as input to neural network and 15 hidden layer neurons in neural network designed. The design of architecture is finalised with maximised accuracy for labelling microcalcification clusters as benign or malignant.

Hybrid Wavelet Features for the Classification of Microcalcification Clusters in Digital Mammograms

Hybrid Wavelet Features for the Classification of Microcalcification Clusters in Digital Mammograms

Tree-based modelling for the classification of mammographic benign and malignant micro-calcification clusters

Computer Aided Detection (CAD) systems are being developed to assist radiologists in diagnosis. For breast cancer the emphasis is shifting from detection to classification of abnormalities. The presented work concentrates on the benign versus malignant classification of micro-calcification clusters, which are a specific type of mammographic abnormality associated with the early development of breast cancer. After segmentation (automatic or manual), tree-based representations were used to distinguish between benign and malignant clusters, which takes into account clinical criteria such as the number of micro-calcifications in the clusters and their distribution and is based on the topology of the trees and the connectivity of the micro-calcifications. The idea of using tree structure based on the distance of individual calcifications for the classification of benign and malignant micro-calcification clusters is novel and closely related to clinical perception. Tree structures used in this study are distinct from decision trees classifiers being used in many machine learning approaches. Initial evaluation on the Digital Database for Screening Mammography (DDSM) data shows promising results, with an accuracy equal to 91 %, which is comparable to state of the art CAD systems and is in line with clinical perception of the morphology and appearance of benign and malignant micro-calcification clusters.

An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine

Breast cancer is the second leading cause of cancer deaths after lung cancer. In order to avoid mortality due to breast cancer, an efficient computer aided diagnosis system for early prediction of breast cancer is needed. In this paper, an efficient computerized system is designed for the classification of Microcalcification Clusters (MC) in digitized mammograms. The proposed system uses Dual Tree M-Band Wavelet Transform (DTMBWT) to represent the digital mammogram in a multiresolution manner and Support Vector Machine (SVM) for classification. The extracted sub band energies from DTMBWT decomposed mammograms are used as distinguishable features for the classification of MCs into either malignant or benign by SVM classifier. The results show that the proposed DTMBWT based classification system achieves 91.83%accuracy on Mammographic Image Analysis Society (MIAS) database images. General Terms Feature extraction, Classification, Energy computation.

Characterisation of microcalcification clusters on 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT): does DBT underestimate microcalcification clusters? Results of a multicentre study.

To compare DBT and FFDM in the classification of microcalcification clusters (MCs) using BI-RADS. This Institutional Review Board-approved study was undertaken in three centres. A total of 107 MCs evaluated with both DBT and FFDM were randomised for prospective reading by six experienced breast radiologists and classified using BI-RADS. The benign/malignant ratio of MC was 66/41. Of 11/107 discordant results, DBT classified MCs as R2 whereas FFDM classified them as R3 in 9 and R4 in 2. Three of these (3/107 = 2.8%) were malignant; 8 (7.5%) were nonmalignant and were correctly classified as R2 on DBT but incorrectly classified as R3 on FFDM. Estimated sensitivity and specificity, respectively, were 100% (95% CI: 91% to 100%) and 94.6% (95% CI: 86.7% to 98.5%) for FFDM and 91.1% (95% CI: 78.8% to 97.5%) and 100% (95% CI: 94.8% to 100%) for DBT. Overall intra- and interobserver agreements were 0.75 (95% CI: 0.61-0.84) and 0.73 (95% CI: 0.62-0.78). Most MCs are scored similarly on FFDM and DBT. Although a minority (11/107) of MCs are classified differently on FFDM (benign MC classified as R3) and DBT (malignant MC classified as R2), this may have clinical relevance. • The BI-RADS classification of MC differs for FFDM and DBT in 11/107 cases • DBT assigned lower BI-RADS classes compared to FFDM in 11 clusters • In 4/107 DBT may have missed some malignant and high-risk lesions • In 7/107 the 'underclassification' on DBT was correct, potentially avoiding unnecessary biopsies • DBT may miss a small proportion of malignant lesions.

On the Classification of Imbalanced Datasets

In recent research the classifications of imbalanced data sets have received considerable attention. It is natural that due to the class imbalance the classifier tends to favour majority class. In this paper we investigate the performance of different methods for handling data imbalance in the microcalcification classification which is a classical example for data imbalance problem. Micro calcifications are very tiny deposits of calcium that appear as small bright spots in the mammogram. Classification of microcalcification clusters from mammograms plays an important role in computer-aided diagnosis for early detection of breast cancer. In this paper, we review in brief the state of the art techniques in the framework of imbalanced data sets and investigate the performance of different methods for microcalcification classification.

ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging

Classification of microcalcification clusters from mammograms plays essential roles in computer-aided diagnosis for early detection of breast cancer, where support vector machine (SVM) and artificial neural network (ANN) are two commonly used techniques. Although some work suggest that SVM performs better than ANN, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve Az. This performance may become much worse when the training samples are imbalanced. As a result, a new strategy namely balanced learning with optimized decision making is proposed to enable effective learning from imbalanced samples, which is further employed to evaluate the performance of ANN and SVM in this context. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from both ANN and SVM has been significantly improved. Although ANN outperforms SVM when balanced learning is absent, the performance from the two classifiers becomes very comparable when both balanced learning and optimized decision making are employed. Consequently, an average improvement of more than 10% in the measurements of F 1 score and Az measurement are achieved for the two classifiers. This has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.

Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach

In this study, we explore a mathematical model to characterize the clustered microcalcifications on mammograms for predicting the pathological classification and grading. Our database consists of both retrospective cases (78 cases) and prospective cases (31 cases) with pathologically diagnosed clusters of microcalcifications on mammograms. The microcalcifications were divided into four grades: grade 0, benign breast disease including mastopathies (n = 12) and fibroadenomas (n = 20); grade 1, well-differentiated infiltrating ductal carcinoma (n = 12); grade 2, moderately differentiated infiltrating ductal carcinoma (n = 38); grade 3, poorly differentiated infiltrating ductal carcinoma (n = 27). A feature parameter, defined as the pattern form factor of microcalcification cluster θ by us, combines five computer-extracted image parameters of microcalcification clusters of those mammograms. In every case, only one imaging was selected for modeling analysis. A total of 109 imagings were adopted in current study. We find the existence of a positive relationship between the feature parameter θ and pathological grading G of microcalcifications in retrospective cases, which was expressed as G = 6.438 + 1.186 × Ln <θ>. The model above has been verified further by the prospective study with a comparative evaluation accuracy of approximately 77.42%. The binary predication simply for both benignancy and malignancy was also included using same but reshuffled data, and the receiver operating characteristic (ROC) analysis was performed with ROC value 0.74351∼0.79891. As one candidate for feature parameter in computer-aided diagnosis, the pattern form factor θ of clustered microcalcifications may be useful to predict the pathological grading and classification of microcalcification clusters on mammography in breast cancer.

Effective recognition of MCCs in mammograms using an improved neural classifier

Computer-aided diagnosis is one of the most important engineering applications of artificial intelligence. In this paper, early detection of breast cancer through classification of microcalcification clusters from mammograms is emphasized. Although artificial neural network (ANN) has been widely applied in this area, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve A z . This performance may become much worse when the training samples are imbalanced. As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from has been significantly improved. An average improvement of more than 10% in the measurements of F 1 score and A z has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.

An Improved Computer-aided Diagnosis Scheme Using the Nearest Neighbor Criterion for Determining Histological Classification of Clustered Microcalcifications

Our purpose was to evaluate the potential usefulness of the nearest neighbor case which was assumed to be the similar case in a CAD scheme for determining the histological classification of clustered microcalcifications. Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after three-month follow-up examination. It included 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 non-invasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. Six objective features on clustered microcalcifications were first extracted from each of the current and the previous images. The nearest neighbor case was then identified by the Euclidean distance in the previous and current feature-space. The histological classification of an unknown new case in question was assumed to be the same as that of the nearest neighbor case which has the shortest Euclidean distance in our database. The classification accuracies were 90.9% for invasive carcinoma, 89.5% for noninvasive carcinoma of the comedo type, 96.0% for noninvasive carcinoma of the noncomedo type, 82.6% for mastopathy, and 93.3% for fibroadenoma. These results were substantially higher than those with our previous CAD scheme. The nearest neighbor criterion was useful in a CAD scheme for determining the histological classification.

Classification of clustered microcalcifications using a Shape Cognitron neural network

A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications. The architecture of S-Cognitron consists of two modules and an extra layer, called 3D figure layer lies in between. The first module contains a shape orientation layer, built with 20 cell planes of low level universal shape features to convert first-order shape orientations into numeric values, and a complex layer, to extract second-order shape features. The 3D figure layer is a feature extract-display layer that extracts the shape curvatures of an input pattern and displays them as a 3D figure. It is then followed by a second module made up of a feature formation layer and a probabilistic neural network-based classification layer. The system is evaluated by using Nijmegen mammogram database and experimental results show that sensitivity and specificity can reach 86.1 and 74.1%, respectively.

Computer Aided Diagnosis of Clustered Microcalcifications Using Artificial Neural Nets

Objective: Development of a fully automated computer application for detection and classification of clustered microcalcifications using neural nets. Material and Methods: Mammographic films with clustered microcalcifications of known histology were digitized. All clusters were rated by two radiologists on a 3 point scale: benign, indeterminate and malignant. Automated detected clustered microcalcifications were clustered. Features derived from those clusters were used as input to 2 artificial neural nets: one was trained to identify the indeterminate clusters, whereas the second ANN classified the remaining clusters in benign or malignant ones. Performance evaluation followed the patient-based receiver operator characteristic analysis. Results: For identification of patients with indeterminate clusters a an Az-value of 0.8741 could be achieved. For the remaining patients their clusters could be classified as benign or malignant at an Az-value of 0.8749, a sensitivity of 0.977 and specificity of 0.471. Conclusions: A fully automated computer system for detection and classification of clustered microcalcifications was developed. The system is able to identify patients with indeterminate clusters, where additional investigations are recommended, and produces a reliable estimation of the biologic dignity for the remaining ones.

Computer assisted diagnosis for digital mammography

This article describes the application of wavelet transform for image enhancement in medical imaging. The initial clinical application is the enhancement of microcalcification clusters (MCCs) in digitized mammograms to improve both their visualization and their detection using computer assisted diagnostic (CAD) methods. The potential universal application for improved visual interpretation of medical images using a computer monitor is also demonstrated. The early detection of MCCs is important in screening programs since their presence is often associated with a high incidence of breast cancer. The enhancement of MCCs is an excellent model for real world evaluation of the wavelet transform. The detection of MCCs presents a significant challenge to the performance characteristics of X-ray imaging sensors and image display monitors since microcalcifications vary in size, shape, signal intensity, and contrast and may be located in areas of very dense parenchymal tissue, making their detection difficult. The classification of MCCs, in turn, as benign or malignant, requires their morphology and detail to be preserved. >