Microcalcifications In Mammograms Research Articles

Automatic Calcification Morphology and Distribution Classification for Breast Mammograms with Multi-task Graph Convolutional Neural Network.

The morphology and distribution of microcalcifications are the most important descriptors for radiologists to diagnose breast cancer based on mammograms. However, it is very challenging and time-consuming for radiologists to characterize these descriptors manually, and there also lacks of effective and automatic solutions for this problem. We observed that the distribution and morphology descriptors are determined by the radiologists based on the spatial and visual relationships among calcifications. Thus, we hypothesize that this information can be effectively modelled by learning a relationship-aware representation using graph convolutional networks (GCNs). In this study, we propose a multi-task deep GCN method for automatic characterization of both the morphology and distribution of microcalcifications in mammograms. Our proposed method transforms morphology and distribution characterization into node and graph classification problem and learns the representations concurrently. We trained and validated the proposed method in an in-house dataset and public DDSM dataset with 195 and 583 cases,respectively. The proposed method reaches good and stable results with distribution AUC at 0.812 ±0.043 and 0.873 ±0.019, morphology AUC at 0.663 ±0.016 and 0.700 ±0.044 for both in-house and public datasets. In both datasets, our proposed method demonstrates statistically significant improvements compared to the baseline models. The performance improvements brought by our proposed multi-task mechanism can be attributed to the association between the distribution and morphology of calcifications in mammograms, which is interpretable using graphical visualizations and consistent with the definitions of descriptors in the standard BI-RADS guideline. In short, we explore, for the first time, the application of GCNs in microcalcification characterization that suggests the potential of using graph learning for more robust understanding of medical images.

Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution

PurposeTo evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography. MethodMammograms were consecutively collected from 5080 patients who underwent breast cancer screening from January 2015 to March 2017. Of these, 93 patients (136 breasts, mean age, 50 ± 7 years) had microcalcifications in their breasts on mammograms. We applied an artificial intelligence model known as a fast SR convolutional neural network to the mammograms. SR and original mammograms were visually evaluated by four breast radiologists using a 5-point scale (1: original mammograms are strongly preferred, 5: SR mammograms are strongly preferred) for the detection, diagnostic quality, contrast, sharpness, and noise of microcalcifications. Mammograms were quantitatively evaluated using a perception-based image-quality evaluator (PIQE). ResultsAll radiologists rated the SR mammograms better than the original ones in terms of detection, diagnostic quality, contrast, and sharpness of microcalcifications. These ratings were significantly different according to the Wilcoxon signed-rank test (p <.001), while the noise score of the three radiologists was significantly lower (p <.001). According to PIQE, SR mammograms were rated better than the original mammograms, showing a significant difference by paired t-test (p <.001). ConclusionAn SR model based on deep learning can improve the visibility of microcalcifications in mammography and help detect and diagnose them in mammograms.

SqueezeU-Net-based detection and diagnosis of microcalcification in mammograms

SqueezeU-Net-based detection and diagnosis of microcalcification in mammograms

Multiregion segmentation of microcalcificationin mammogram images by using Parametric Kernel Graph Cut algorithm

Early detection of breast cancer can be detected through screening mammography. However, the potential abnormality such as microcalcification can hardly be differentiated by the radiologists due to the tiny size, which sometimes be hidden behind the density of breast tissue. Therefore, image segmentation technique is required. This paper proposes the potential use of Parametric Kernel Graph Cut Algorithm in segmenting microcalcification. The performances of this method were measured based on accuracy, sensitivity, Dice and Jaccard coefficient. All the experimental results generated satisfying results, whereby all images produced the average of 91.67% for Dice coefficient and 84.72% for Jaccard coefficient. Meanwhile, both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut algorithm had proved its ability to segment the microcalcification robustly and efficiently.

RETRACTED ARTICLE: Dynamic cross propagation algorithm based detection of micro calcification in digital mammogram

Micro calcification in mammograms may be considered early signs of breast cancer. However, their detection by a variety of factors is a very challenging task to find cancer in an instant starting stage. A breast compression, more difficult breast anatomy, and in some cases, inaccessible size calculations, as well as the significant variation of low contrast, inherent to mammograms. Therefore a computerized image processing scheme is implemented for detecting early-stage Microcalcification in mammograms. Necessarily the masses and microcalcification may be a prominent early symptom of breast cancer. This proposed Dynamic Cross Propagation Algorithm (DCPA) tested several images from digital database mammography for cancer research and diagnosis. In the first stage of preprocessing will proceed for reducing noise in the original image. In the second stage to Finding of interest, which can be done by using a superior region of interest (SROI) detection and region segmentation, is in the area of a suspicious mass on a mammogram. After detecting suspicious mass on the mammogram, the features like mean, standard deviation, variance, skewness, and entropy values are obtained for further classification. In the Fourth stage the feature result, based classification using the proposed Dynamic Cross Propagation Algorithm (DCPA) compiles to find the loss function in the micro image and compare it with the train the model. In this method, SROI fractal dimensions have computed the value that corresponds with training data to find the suspicious microcalcification regions. The proposed system shows a high rate of true positives and a low rate of false-positive, a superior performance compared to the conventional method. The performance of the proposed DCPA technique is analyzed in terms of precision, recall and F-measure. The experimental results produced a classification that gives better accuracy 98.38% of which indicates that the proposed system is more promising in classification digital mammograms.

Enhancement of mammogram images by using entropy improvement approach

Breast cancer in women has been the most often diagnosed cancer. Digital mammogram becomes the most effective imaging method to detect breast cancer in early stage. In breast cancer screening, radiologists typically miss about 10–30% of tumors due to the speculated margins of lesions. Mammogram is a low contrast image whose quality needs to be improved for better interpretation. The performance of the validation of pre-processing methods for mammogram image enhancement is done by performance metrics such as peak signal to noise ratio (PSNR) and mean square error (MSE). Good filtering technique having higher PSNR and low MSE value. Experimental results on the digital database for screening mammography images shown that the non-local mean filter is better for mammogram image enhancement. Further we proposed mammogram images enhancement by entropy improvement method by considering non-local filtered images. These methodologies could add to the effective discovery of masses and micro calcifications in mammograms.

The Detection and Classification of Microcalcifications in the Visibility-Enhanced Mammograms Obtained by using the Pixel Assignment-Based Spatial Filter

In this paper, we proposed a computer aided diagnosis (CAD) system which has the pixel assignment-based a spatial filter to enhance the visibility of microcalcifications in mammograms. ...

Quantum inspired algorithm for microcalcification detection in mammograms

This paper introduces a novel methodology based on quantum signal processing (QSP) and cellular automata (CA) to detect microcalcifications in mammograms. The methodology includes algorithms to detect microcalcification in binary and grayscale images. Essentially, quantum algorithms are used to improve edge detection results and CA are used to enhance the definition of the microcalcification in both groups of images. In grayscale images, CA are used to identify areas having a high probability of being microcalcifications. Benchmark images from the Mammographic Image Analysis Society (Mini-MIAS) digital mammogram database and the Digital Database for Screening Mammography (DDSM), which include mammograms showing several abnormalities previously evaluated by experts, were used in our study. Experiments using the two quantum processing algorithms were conducted, and the results were compared with those of other approaches for microcalcification detection.

Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

Breast cancer is most common disease in women of all ages. To identify &amp; confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation &amp; classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly &amp; requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification &amp; testing images are pre-processed to resize, enhance &amp; filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature.

Elicitation and representation of expert knowledge for computer aided diagnosis in mammography.

To study how professional radiologists describe, interpret and make decisions about micro-calcifications in mammograms. The purpose was to develop a model of the radiologists' decision making for use in CADMIUM II, a computerized aid for mammogram interpretation that combines symbolic reasoning with image processing. In Study 1, eleven radiologists were asked to 'think out loud' as they interpreted 20 sets of calcifications. Participants used 159 terms to describe calcifications. We used these data to design a scheme with 50 descriptors. In Study 2, ten radiologists used the scheme to describe 40 sets of calcifications. We assessed the capacity of the terms to discriminate between benign and malignant calcifications, testing them against radiologists' assessments of malignancy and follow-up data. The descriptors that were found to be the most discriminating in Study 2 were included in CADIMUM II's knowledge base. They were represented as arguments for either a benign or a malignant diagnosis. These arguments are the central component of the decision support provided by the system. Other components are: image processing algorithms for the detection and measurement of calcifications and a set of rules that use the measures to decide which of the arguments apply to a given set of calcifications. Preliminary evaluations of the CADMIUM II prototype reinforce the value of representing explicitly decision making processes in computer aided mammography and of deriving these processes from image processing measurements. Decision support is presented here at a level of description that is both relevant and meaningful to the user.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value <10−4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis

Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature.Graphical Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis

Comparison of the Geometric and Digital Magnification to Detect Micro-Calcification in Mammogram

Comparison of the Geometric and Digital Magnification to Detect Micro-Calcification in Mammogram

Improvement of the Image Sub-Segmentation for Identification and Differentiation of Atypical Regions

A new sub-segmentation method has been proposed in 2009 which, in digital images, help us to identify the typical pixels, as well as the less representative pixels or atypical of each segmented region. This method is based on the Possibilistic Fuzzy c-Means (PFCM) clustering algorithm, as it integrates absolute and relative memberships. Now, the segmentation problem is related to isolate each one of the objects present in an image. However, and considering only one segmented object or region represented by gray levels as its only feature, the totality of pixels is divided in two basic groups, the group of pixels representing the object, and the others that do not represent it. In the former group, there is a sub-group of pixels near the most representative element of the object, the prototype, and identified here as the typical pixels, and a sub-group corresponding to the less representative pixels of the object, which are the atypical pixels, and generally located at the borders of the pixels representing the object. Besides, the sub-group of atypical pixels presents greater tones (brighter or towards the white color) or smaller tones (darker or towards black color). So, the sub-segmentation method offers the capability to identify the sub-region of atypical pixels, although without performing a differentiation between the brighter and the darker ones. Hence, the proposal of this work contributes to the problem of image segmentation with the improvement on the detection of the atypical sub-regions, and clearly recognizing between both kind of atypical pixels, because in many cases only the brighter or the darker atypical pixels are the ones that represent the object of interest in an image, depending on the problem to be solved. In this study, two real cases are used to show the contribution of this proposal; the first case serves to demonstrate the pores detection in soil images represented by the darker atypical pixels, and the second one to demonstrate the detection of microcalcifications in mammograms, represented in this case by the brighter atypical pixels.

An Approach for Enhancement of Microcalcifications in Mammograms

Breast cancer is one of the prominent causes of female mortality in the world, and microcalcification clusters are the important indicators for breast cancer. Mammography is a useful procedure for revealing these indicators at an early stage. But the manual interpretation of microcalcifications is difficult due to low contrast with the background parenchymal tissue. This makes it hard to judge the size, shape and morphology of the microcalcifications. In this paper a methodology, which is a combination of morphological operations, unsharp masking and Gaussian filter, has been proposed for enhancement of mammograms to bring out the tiny details of microcalcifications present in a variety of nonhomogeneous background tissues while restoring their shape and size. For experiment the mammogram images, collected from Digital Database for Screening Mammography, have been used and the results are compared to standard methods like contrast limited adaptive histogram equalization, multi scale top-hat transform based algorithm and bi-histogram equalization with adaptive sigmoid functions. The results from both the qualitative and quantitative evaluations suggest that the proposed methodology is very effective.

Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform

Abstract Diagnosis of benign and malignant microcalcifications in digital mammography using Computer-aided Diagnosis (CAD) system is critical for the early diagnosis of breast cancer. Wavelet transform based diagnosis methods are effective to accomplish this task, but limited by representing the correlation within each wavelet scale, these methods neglect the correlation between wavelet scales. In this paper, we apply the hidden Markov tree model of dual-tree complex wavelet transform (DTCWT-HMT) for microcalcification diagnosis in digital mammography. DTCWT-HMT can effectively capture the correlation between different wavelet coefficients and model the statistical dependencies and non-Gaussian statistics of real signals, is used to characterize microcalcifications for the diagnosis of benign and malignant cases. The combined features which consist of the DTCWT-HMT features and the DTCWT features are optimized by genetic algorithm (GA). Extreme learning machine (ELM), an efficient learning theory is employed as the classifier to diagnose the benign and malignant microcalcifications. The validity of the proposed method is evaluated on the Nijmegen, MIAS and DDSM datasets using area under curve (AUC) of receiver operating characteristic (ROC). The AUC values of 0.9856, 0.9941 and 0.9168 of the proposed method are achieved on Nijmegen, MIAS and DDSM, respectively. We compare the proposed method with state-of-the-art diagnosis methods, and the experimental results show the effectiveness of the proposed method for the diagnosis of the benign and malignant microcalcifications in mammograms in terms of the accuracy and stability.

Microcalcification cluster detection using multiscale products based Hessian matrix via the Tsallis thresholding scheme

Breast cancer is an essential health issue and more than one million women die of breast cancer each year in the world. Primary prevention seems impossible since the causes of this disease still remain unknown. Early detection and diagnosis is the key for breast cancer control. Clusters of microcalcifications in mammogram have been mainly targeted as an early sign of breast cancer and their earliest detection is vital to reduce the mortality rate. Since the size of microcalcification is very tiny and may be overlooked by the observing radiologist, a Computer Aided Diagnosis (CAD) system has been developed for efficient microcalcification detection and it eliminates the operator dependency. In order to determine the presence of microcalcification clusters in the mammogram, special attention is paid to the analysis of the structure and brightness of the mammogram tissues. The detection of microcalcification clusters is achieved by the following computerized approach. The nodular structured microcalcifications in the abnormal mammogram image are detected based on multiscale products of eigenvalues of the Hessian matrix. The detected image contains calcifications along with background information. To eliminate the unnecessary background information, the response image coming out from Hessian matrix approach is passed to the thresholding technique such as probability density function based Tsallis entropy, in which the potential microcalcifications are segmented efficiently. The proposed method is evaluated with the DDSM, MIAS and UCSF databases, which included a wide spectrum of difficult-to-detect cases. The detection performance of the proposed method has been evaluated by using 234 mammograms containing 171 microcalcification clusters. The detection method has a true positive ratio of 97.08% with 0.45 false positives per image. The proposed algorithm is positively evaluated through the clinical study. The main merit of the system is that it maintains the shape and size of microcalcifications.

Global detection approach for clustered microcalcifications in mammograms using a deep learning network

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69clusters/image, compared to 1.17clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

Microcalcification Segmentation from Mammograms: A Morphological Approach

This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s.

Image descriptors in radiology images: a systematic review

Clinical decisions are sometimes based on a variety of patient's information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors--computational algorithms that present a summarized description of image regions--became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures--e.g., intensity, texture, shape--in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer's disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.