Microcalcifications In Digital Mammograms Research Articles

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.

Computer-aided mammography techniques for detection and classification of microcalcifications in digital mammograms

Recent techniques that are developed in computer-aided mammography (CAM) produce more accurate results in detection and diagnosis of microcalcifications in its earlier state that can lead to breast cancers among women. These techniques aim at the reduction of false positive rates through which the number of biopsies and surgeries can be greatly reduced. This paper gives a detailed study of the existing techniques available in CAM for the segmentation and classification of the microcalcifications present in the di0067ital mammograms which help the radiologists to take quick and accurate diagnosis decisions.

Computer-aided mammography techniques for detection and classification of microcalcifications in digital mammograms

Computer-aided mammography techniques for detection and classification of microcalcifications in digital mammograms

A New Image Mining Approach for Detecting Micro-Calcification in Digital Mammograms

ABSTRACTAlthough mammography is typically the best method to detect breast cancer, it does not recognize 3–20% of the cancer cases. Mammography has established itself as the most efficient technique for detecting tiny cancerous tumor and micro-calcifications are the most difficult to detect since they are very small (0.1–1.0 mm) and they are almost contrasted against the images background. The main purpose of this paper is to provide a new method for the automatic diagnosis of micro-calcification in digital mammograms. It is based on image mining, and the results show 97.35% accuracy, which is improved than the previous works. Tests are based on the standard images data corpus, MIAS. The practical result of this research is registered as an invention in the Patents and Industrial Property Registration Organization numbered as 83119.

Early Detection of Microcalcification in Digital Mammograms using Neural Network

Early Detection of Microcalcification in Digital Mammograms using Neural Network

A high-sensitivity computer-aided system for detecting microcalcifications in digital mammograms using curvelet fractal texture features

A high-sensitivity computer-aided system for detecting microcalcifications in mammographic images requires an efficient feature extraction stage and an enhanced pattern recognition technique for classification. In this paper, a new and an efficient texture feature extraction method, curvelet-based fractal texture analysis is proposed. The system consists of two main stages. In the first stage, the suspicious microcalcification regions are separated from the normal tissues using curvelet layers from which the fractal dimensions are computed to describe the decomposed and oriented texture patterns. The decomposition of the input image is done using the curvelet layers. In the second stage, an ensembled fully complex-valued relaxation network classifier is used for classifying mammograms. The proposed system exhibits superior performance in terms of high true positive rate and low false positive rate, in comparison with the existing techniques. The experimental results yielded a classification accuracy of 98.18%, which indicates that curvelet fractal is a promising tool for analysis and classification of digital mammograms.

Radiological technologists' performance for the detection of malignant microcalcifications in digital mammograms without and with a computer-aided detection system.

The aim of this study was to investigate the diagnostic performance of radiological technologists (RTs) in the detection of malignant microcalcifications and to evaluate how much computer-aided detection (CADe) improved their performances compared with those by expert breast radiologists (BRs). Six board-certified breast RTs and four board-certified BRs participated in a free-response receiver operating characteristic observer study. The dataset consisted of 75 cases (25 malignant, 25 benign, and 25 normal cases) of digital mammograms, selected from the digital database for screening mammography provided by the University of South Florida. Average figure of merit (FOM) of the RTs' performances was statistically analyzed using jack-knife free-response receiver operating characteristic and compared with that of expert BRs. The detection performance of RTs was significantly improved by using CADe; average sensitivity was increased from 46.7% to 56.7%, with a decrease in the average number of false positives per case from 0.19 to 0.13. Detection accuracy of an average FOM was improved from 0.680 to 0.816 ([Formula: see text]) and the difference in FOMs between RTs and radiologists failed to reach statistical significance. RTs' performances for the identification of malignant microcalcifications on digital mammography were sufficiently high and comparable to those of radiologists by using CADe.

Segmentation for the enhancement of microcalcifications in digital mammograms.

Microcalcification clusters appear as groups of small, bright particles with arbitrary shapes on mammographic images. They are the earliest sign of breast carcinomas and their detection is the key for improving breast cancer prognosis. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. This work is devoted to developing a system for the detection of microcalcification in digital mammograms. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), we first selected the region of interest (ROI) in order to demarcate the breast region on a mammogram. Segmenting region of interest represents one of the most important stages of mammogram processing procedure. The proposed segmentation method is based on a filtering using the Sobel filter. This process will identify the significant pixels, that belong to edges of microcalcifications. Microcalcifications were detected by increasing the contrast of the images obtained by applying Sobel operator. In order to confirm the effectiveness of this microcalcification segmentation method, the Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithm are employed for the classification task using cross-validation technique.

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.

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.

GAUSSIAN MIXTURE MODEL BASED CLASSIFICATION OF MICROCALCIFICATION IN MAMMOGRAMS USING DYADIC WAVELET TRANSFORM

Breast cancer is a serious health related issue for women in the world. Cancer detected at premature stages has a higher probability of being cured, whe reas at advanced stages chances of survival are ble ak. Screening programs aid in detecting potential breas t cancer at early stages of the disease. Among the various screening programs, mammography is the proven standard for screening breast cancer, because even small tumors can be detected on mammograms. In this study, a novel feature extraction technique based on dyadic wavelet transform for classificatio n of microcalcification in digital mammograms is proposed. In the feature extraction module, the hig h frequency sub-bands obtained from the decomposition of dyadic wavelet transform is used t o form innovative sub-bands. From the newly constructed sub-bands, the features such as energy and entropy are computed. In the classification module, the extracted features are fed into a Gauss ian Mixture Model (GMM) classifier and the severity of given microcalcification; benign or malignant ar e given. A classification accuracy of 95.5% is obtained using the proposed approach on DDSM database.

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.

Morphological Enhancement of Microcalcifications in Digital Mammograms

Mammography is a commonly used technique for early detection of breast cancer. In mammograms, microcalcifications show low contrast margin with the background parenchymal tissue (specifically when the background tissue type is fibroglandular) as a result, subjective analysis of these calcifications with respect to their size, shape and morphology presents a daunting challenge even for experienced radiologists. Thus the present work investigates the potential of two morphological techniques i.e., top-hat morphological processing and h-dome morphological processing for enhancement of microcalcifications embedded in variety of background tissue types including fatty, glandular and fibroglandular tissues while restoring their shape and size. The enhancement results are also compared with standard contrast limited adaptive histogram equalization method. For subjective analysis, 25 synthetic images with simulated microcalcifications of various shapes and sizes are used. Objective analysis is carried out on 50 mammographic images taken from benchmark dataset (McGill University mammographic database) by computing quantitative indices like contrast improvement ratio and detail variance/background variance ratios. After rigorous experimentation on both synthetic and benchmark data set it was observed that h-dome morphological processing (with h = 60) is ideally suited for enhancement of microcalcifications while restoring their shape and size.

Nonsubsampled Contourlet Transform Based Classification of Microcalcification in Digital Mammograms

In this paper, an algorithm for the classification of microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM) is presented. The classification of microcalcification is achieved by extracting the microcalcification features using NSCT with different scales. SVM classifier is used to classify the mammogram images based on the extracted microcalcification features. 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 of over 90% for all cases

Wavelet Analysis and Orthogonal Moments based Classification of Microcalcification in Digital Mammograms

Problem statement: An important early sign of breast cancer is the clusters of micro calcifications in digital mammograms. To assist radiologists in the diagnosis of mammographic clusters a novel hybrid algorithm has been developed. Approach: A method, for detecting micro calcification in mammograms based on combined feature set with Ant Colony Optimization (ACO) was proposed. The diagonal matrix ‘S’ obtained from the Singular Value Decomposition (SVD) of LL band of wavelet transform was used as one of the feature set for classification of mammogram. A new approach for detecting micro calcifications in mammograms employing Jacobi Moments was proposed. The set of Jacobi polynomials were orthogonal and this ensured minimal information redundancy between the moments. Results and Conclusion: Jacobi moments include the properties of well-known Zernike, Legendre and Tchebichef moments. Ant Colony Optimization (ACO) was used for reducing the Jacobi feature set dimensionality through selecting a subset of features that performed well in the classification phase. The selected Jacobi feature set were combined with ‘S’ matrix to achieve the better classification rate of over 90%.

A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms

Early detection of microcalcification clusters in breast tissue will significantly increase the survival rate of the patients. Radiologists use mammography for breast cancer diagnosis at early stage. It is a very challenging and difficult task for radiologists to correctly classify the abnormal regions in the breast tissue, because mammograms are noisy images. To improve the accuracy rate of detection of breast cancer, a novel intelligent computer aided classifier is used, which detects the presence of microcalcification clusters. In this paper, an innovative approach for detection of microcalcification in digital mammograms using Swarm Optimization Neural Network (SONN) is used. Prior to classification Laws texture features are extracted from the image to capture descriptive texture information. These features are used to extract texture energy measures from the Region of Interest (ROI) containing microcalcification (MC). A feedforward neural network is used for detection of abnormal regions in breast tissue is optimally designed using Particle Swarm Optimization algorithm. The proposed intelligent classifier is evaluated based on the MIAS database where 51 malignant, 63 benign and 208 normal images are utilized. The approach has also been tested on 216 real time clinical images having abnormalities which showed that the results are statistically significant. With the proposed methodology, the area under the ROC curve (A ( z )) reached 0.9761 for MIAS database and 0.9138 for real clinical images. The classification results prove that the proposed swarm optimally tuned neural network highly contribute to computer-aided diagnosis of breast cancer.

Breast cancer evaluation by fluorescent dot detection using combined mathematical morphology and multifractal techniques

BackgroundFluorescence in situ hybridization (FISH) is very accurate method for measuring HER2 gene copies, as a sign of potential breast cancer. This method requires small tissue samples, and has a high sensitivity to detect abnormalities from a histological section. By using multiple colors, this method allows the detection of multiple targets simultaneously. The target parts in the cells become visible as colored dots. The HER-2 probes are visible as orange stained spots under a fluorescent microscope while probes for centromere 17 (CEP-17), the chromosome on which the gene HER-2/neu is located, are visible as green spots.MethodsThe conventional analysis involves the scoring of the ratio of HER-2/neu over CEP 17 dots within each cell nucleus and then averaging the scores for a number of 60 cells. A ratio of 2.0 of HER-2/neu to CEP 17 copy number denotes amplification. Several methods have been proposed for the detection and automated evaluation (dot counting) of FISH signals. In this paper the combined method based on the mathematical morphology (MM) and inverse multifractal (IMF) analysis is suggested. Similar method was applied recently in detection of microcalcifications in digital mammograms, and was very successful.ResultsThe combined MM using top-hat and bottom-hat filters, and the IMF method was applied to FISH images from Molecular Biology Lab, Department of Pathology, Wielkoposka Cancer Center, Poznan. Initial results indicate that this method can be applied to FISH images for the evaluation of HER2/neu status.ConclusionsMathematical morphology and multifractal approach are used for colored dot detection and counting in FISH images. Initial results derived on clinical cases are promising. Note that the overlapping of colored dots, particularly red/orange dots, needs additional improvements in post-processing.

Computer aided system for segmentation and visualization of microcalcifications in digital mammograms.

Two methods for segmentation and visualization of microcalcifications in digital or digitized mammograms are described. First method is based on modern mathematical morphology, while the second one uses the multifractal approach. In the first method, by using an appropriate combination of some morphological operations, high local contrast enhancement, followed by significant suppression of background tissue, irrespective of its radiology density, is obtained. By iterative procedure, this method highly emphasizes only small bright details, possible microcalcifications. In a multifractal approach, from initial mammogram image, a corresponding multifractal "images" are created, from which a radiologist has a freedom to change the level of segmentation. An appropriate user friendly computer aided visualization (CAV) system with embedded two methods is realized. The interactive approach enables the physician to control the level and the quality of segmentation. Suggested methods were tested through mammograms from MIAS database as a gold standard, and from clinical praxis, using digitized films and digital images from full field digital mammograph.

Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features

We investigated the performance of clustered microcalcifications (MCs) recognition in digital mammograms by using combined model-based and statistical textural features. Twenty mammograms containing 25 areas of MCs from the MIAS MiniMammogram database were used to test the performance of our method. In the first stage, a wavelet filter and two thresholds were used to detect suspicious MCs from the mammograms. In the second stage, textural features based on Markov random field (MRF) and fractal models together with statistical textural features based on the surrounding region-dependence method (SRDM) were extracted from the neighborhood of the suspicious MCs and were classified by a three-layer BPNN. The free-response operating characteristic (FROC) curve was used to evaluate the performance of classification and compare our results with that presented in the literature from four other studies. The results of the experiments suggest that the combined model-based and statistical textural features are suitable for characterizing microcalcifications and capable of supporting a reliable and effective MCs detection. In particular, a true positive rate of about 94% is achieved at the rate of 1.0 false positive per image, or the false positives per image can be reduced to 0.65 FPs/image at the rate of true positive of about 90%.