Detection Of Microcalcifications Research Articles

Computational reader design and statistical performance evaluation of an in-silico imaging clinical trial comparing digital breast tomosynthesis with full-field digital mammography.

A recent study reported on an in-silico imaging trial that evaluated the performance of digital breast tomosynthesis (DBT) as a replacement for full-field digital mammography (FFDM) for breast cancer screening. In this in-silico trial, the whole imaging chain was simulated, including the breast phantom generation, the x-ray transport process, and computational readers for image interpretation. We focus on the design and performance characteristics of the computational reader in the above-mentioned trial. Location-known lesion (spiculated mass and clustered microcalcifications) detection tasks were used to evaluate the imaging system performance. The computational readers were designed based on the mechanism of a channelized Hotelling observer (CHO), and the reader models were selected to trend human performance. Parameters were tuned to ensure stable lesion detectability. A convolutional CHO that can adapt a round channel function to irregular lesion shapes was compared with the original CHO and was found to be suitable for detecting clustered microcalcifications but was less optimal in detecting spiculated masses. A three-dimensional CHO that operated on the multiple slices was compared with a two-dimensional (2-D) CHO that operated on three versions of 2-D slabs converted from the multiple slices and was found to be optimal in detecting lesions in DBT. Multireader multicase reader output analysis was used to analyze the performance difference between FFDM and DBT for various breast and lesion types. The results showed that DBT was more beneficial in detecting masses than detecting clustered microcalcifications compared with FFDM, consistent with the finding in a clinical imaging trial. Statistical uncertainty smaller than 0.01 standard error for the estimated performance differences was achieved with a dataset containing approximately 3000 breast phantoms. The computational reader design methodology presented provides evidence that model observers can be useful in-silico tools for supporting the performance comparison of breast imaging systems.

Reduction of false positives in the screening CAD tool for microcalcification detection

Breast cancer is one of the leading causes of cancer deaths among women worldwide. Early diagnosis of breast cancer can help in reducing the mortality rate. The major challenge in the early diagnosis of breast cancer is the fewer number of radiologists available per million population in developing countries. The total number of radiologists is less than 30 in many third world countries. Since majority of the screening mammograms are normal or do not show any cancer signs, there is need of a screening computer-aided diagnosis (CAD) tool that can detect normal mammograms correctly and thereby reduce the burden on radiologists. Thus, a screening CAD is developed that is able to detect microcalcification clusters in mammogram with 100% sensitivity on the subset of DDSM, INbreast and PGIMER-IITKGP databases at lower false positives as compared with state of the art methods. The synthetic minority over-sampling technique and the majority class under-sampling based on data distribution are used to improve the classifiers performance by reducing the false positives. An approach based on principal component analysis is proposed to further reduce the false positives by removing the vascular calcifications that are not of any clinical significance and may increase the false positives.

Recall and Outcome of Screen-detected Microcalcifications during 2 Decades of Mammography Screening in the Netherlands National Breast Screening Program.

Background Trends in the detection of suspicious microcalcifications at mammography screening and the yield of these lesions after recall are unknown. Purpose To determine trends in recall and outcome of screen-detected microcalcifications during 20 years of mammography screening. Materials and Methods The authors performed a retrospective analysis of a consecutive series of 817 656 screening examinations (January 1997 to January 2017) in a national breast screening program. In 2009-2010 (transition period), screen-film mammography (SFM) was gradually replaced by full-field digital mammography (FFDM). The recalls of suspicious microcalcifications from all radiology reports and pathologic outcome of recalled women with 2-year follow-up were analyzed. Screening outcome in the era of SFM (1997-2008), the transition period (2009-2010), and the era of FFDM (2011-2016) were compared. Trends over time and variations between the SFM and FFDM periods were expressed by using proportions with 95% confidence intervals (CIs). In cases where the analysis based on the CI confirmed clear periods (eg, before and after introduction of FFDM), pre- and postchange outcomes were compared by using χ2 tests. Results A total of 18 592 women (median age, 59 years; interquartile range, 14 years) were recalled at mammography screening, 3556 of whom had suspicious microcalcifications. The recall rate for microcalcifications increased from 0.1% in 1997-1998 to 0.5% in 2015-2016 (P < .001). This was temporally associated with the change from SFM to FFDM. The recalls yielding ductal carcinoma in situ (DCIS) increased from 0.3 per 1000 screening examinations with SFM to 1.1 per 1000 screening examinations with FFDM (P < .001), resulting in a decrease in the positive predictive value for recall for suspicious microcalcifications from 51% to 33% (P < .001). More than half of all DCIS lesions were high grade (52.6%; 393 of 747). The distribution of DCIS grades was stable during the 20-year screening period (P = .36). Conclusion The recall rate for suspicious microcalcifications at mammographic screening increased during the past 2 decades, whereas the ductal carcinoma in situ detection rate increased less rapidly, resulting in a lower positive predictive value for recall. © RSNA, 2020.

Detection of Microcalcifications on Mammograms

Breast cancer is a common disease of women. The number of new cases diagnosed in Algeria is increasingly high and it is the first cause of cancer related deaths for women. The microcalcifications are considered the primary sign of breast cancer. The early detection of these allows doctors to take the necessary measures for the treatment of this pathology. Medical imaging has made possible enormous progress in the field of diagnosis and provides an important contribution to the care of patients. This article proposes an approach for the segmentation and detection of microcalcifications in mammographic images based on wavelets, K- means, and the windows of Parzen, in order to detect the presence of breast cancer as early as possible and to avoid radical treatment such as the removal of the breast.

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-of-the-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

An Automated Breast Micro-Calcification Detection and Classification Technique Using Temporal Subtraction of Mammograms

Radiologists worldwide use mammography as a reliable tool for breast cancer screening. However, mammography assessment is challenging even for well-trained radiologists, leading to a pressing need for Computer Aided Diagnosis (CAD) systems. In this work, a novel technique for the detection and classification of breast Micro-Calcifications (MCs), which are diagnostically significant but difficult to detect findings, is presented. The proposed method is based on the subtraction of temporally sequential mammogram pairs, after pre-processing and image registration, followed by machine-learning. The classification was performed using several features extracted from the subtracted mammograms and selected during training to optimize the accuracy of the results. Six classifiers were tested in a leave-one-patient-out, 4, 5 and 10 fold cross-validation process. This technique was evaluated on a unique dataset, consisting of temporal sequences of mammograms from 80 patients taken between 1 to 6 years apart. The resulting 320 mammograms were reviewed by 2 radiologists who precisely marked each MC location. The accuracy of classifying MCs as benign or suspicious improved from 91.42% without temporal subtraction and an Ensemble of Decision Trees (EDT), to 99.55% with the use of sequential mammograms and Support Vector Machines (SVMs) with leave-one-patient-out validation. The improvement was statistically significant (p-value <; 0.005). These results verify the accuracy and the effectiveness of the proposed technique should to be further evaluated on a larger dataset.

Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means.

Objective:The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography.Methods:In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism. Results:According to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image. Conclusion:The comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.

Detection of Micro Calcifications in Mammogram Images Using Texture Analysis and Logistic Regression

Micro-calcification in the breast is a symptom of breast cancer. Therefore, detection of micro-calcification in mammogram image plays an important role in the early diagnosis of breast cancer. Because the mammogram images are 2-dimensional, different tissues in the breast are seen on top of each other. Therefore, it is a compelling task for radiologists to identify the masses found in mammogram images. There are different methods for detecting micro-calcification in mammogram images. In this study, different image processing techniques were applied on mammogram images and a region of 80x80 pixel was taken from breast tissue. Texture features of this region were extracted using co-occurrence matrix and classified by logistic regression analysis. Classification success of 88% was achieved with the proposed model.

Varying the Color of Mammography Display Improves the Detectability of Microcalcifications

Purpose: This study assessed whether varying the color of the display improves the detectability of microcalcifications on mammography. Materials and Methods: The American College of Radiology (ACR) 156 mammographic phantom was imaged under three different conditions. Ten observers evaluated the depiction of 30 phantom microcalcifications presented in six color-scales: red, green, yellow, blue, and cyan. Differences in the detectability of macrocalcifications and eye and psychological fatigue among the different color scales were assessed. Results: Yellow-scale images improved the detectability of microcalcifications to a significantly greater extent than did the other colors: relative to blue and red, P &lt; 0.01; relative to gray, green, and cyan, P &lt; 0.05. The cyan display induced the least eye fatigue. While no difference in eye fatigue induced by the yellow and gray displays was found, displays of these colors were associated with significantly less eye fatigue than the green-scale (P &lt; 0.01), red-scale, and blue-scale displays (P &lt; 0.001). Conclusion: The detectability of microcalcifications on mammography can be improved by changing the color scale in which mammograms are visualized from gray to yellow.

Impact of Colorized Display of Mammograms on Lesion Detection.

To assess the effect of the colorized display of digital mammograms on observer detection of subtle breast lesions. Three separate observer studies compared detection performance using grayscale versus color display of 1) low-contrast mass-like objects in a standardized mammography phantom; 2) simulated microcalcifications in a background of normal breast parenchyma; and 3) standard-of-care clinical digital mammograms with subtle calcifications and masses. Colorization of the images was done by displaying each image pixel in blue, green, and red hues, or gray, maintaining DICOM-calibrated luminance scale and consistent luminance range. For the simulated calcifications and clinical mammogram studies, comparison of detection rates was computed using McNemar's test for paired differences. For the phantom study, mass-like object detection was significantly better using a green colormap than grayscale (73.3% vs 70.8%, P = .009), with no significant improvement using blue or red colormaps (72.6% and 72.5%, respectively). For simulated microcalcifications, no significant difference was noted in detection using the green colormap, as compared with grayscale. For clinical digital screening mammograms, no significant difference was noted between gray and green colormaps for detection of microcalcifications. Green color display, however, resulted in decreased sensitivity for detection of subtle masses (63% vs 69%, P = .03). Although modest improvement was demonstrated for a detection task using colorized display of a standard mammography phantom, no significant improvement was demonstrated using a color display for a simulated clinical detection task, and actual clinical performance was worse for colorized display of mammograms in comparison to standard grayscale display.

Contrast Enhancement of Mammograms and Microcalcification Detection

Mammography is an operative procedure for early detection of cancer present in breast. However, the pathological changes of the breast are difficult to interpret from low contrast mammograms. This research proposes a method to enhance the contrast of the mammogram that uses Non-subsampled contourlet transform (NSCT) based edge information. Instead of a directional filter bank in the conventional NSCT structure, this paper uses multiscale non-separable edge filters. These edge filters outputs intrinsic edge structure information based on simplified hyperbolic tangent function applied with two polarized schemes. This edge information further used to improve the local contrast. Adaptive histogram equalization (AHE) also used to increase the overall contrast of mammogram. Improved detection of microcalcification (MC) from enhanced mammogram images shows the success of this algorithm. This method has better enhancement measure (EME) than AHE and unsharp based mammogram enhancement method.

Detection of Abnormalities in Digital Mammogram Images by Analysing Optical Parameters

Breast cancer is one of the major lifethreatening cancer that tremendously affects women. According to the survey, the mortality rate of females is increasing due to the belated detection of breast cancer. In recent years, Mammograms plays a vital role in breast cancer detection. In this study, an algorithm is used to detect the microcalcification of the mammogram by calculating its optical characteristics. The considered optical characteristics are the reflection coefficient, absorption coefficient, and transmission coefficient are calculated for the binned mammogram digital images are calculated. The total conservation of energy condition, RC + TC + AC =1 is satisfied with the obtained results. For each image, statistical features are extracted, analyzed and are classified with the help of SVM and ANN classifier. This algorithm is verified with about 30% of MIAS database images. This proposed algorithm acts as a good classifier for the detection of microcalcification thereby considering three optical characteristics.

A multi-context CNN ensemble for small lesion detection.

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.

Microc alcification Segmentation Using Modified U-net Segmentation Network from Mammogram Images

Breast cancer is the most common aggressive cancer in women while the early detection of this cancer can reduce the aggressiveness. But it is challenging to identify breast cancer features such as micro-calcification from mammogram images by the human eye because of its size and appearance. Therefore, the automatic detection of micro-calcification is essential for diagnosis and proper treatment. This work introduces an automated approach and segments any micro-calcification in the mammogram images. At first, the preprocessing applications of images are applied to enhance the image. After that, the breast region is segmented from the pectoral region. The suspicious regions are detected using fuzzy C-means clustering algorithm and divided them into negative and positive patches. This procedure eliminates the manual labelling of the region of interest. The positive patches which contain micro-calcification pixels are taken to train a modified U-net segmentation network. Finally, the trained network is utilised to segment the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the segmentation accuracy of the micro-calcification regions. The proposed system is trained up with a Digital Database for Screening Mammography (DDSM), which is prepared by the University of South Florida, USA. We obtain 98.5% F-measure and 97.8% Dice score respectively. Besides, Jaccard index is 97.4%. The average accuracy of the proposed method is 98.2% which provides better performance than state-of-the-art methods. This work can be embedded with the real-time mammography system.

Objective assessment of task performance: a comparison of two FFDM detectors using an anthropomorphic breast phantom.

Current digital mammography systems primarily employ one of two types of detectors: indirect conversion, typically using a cesium-iodine scintillator integrated with an amorphous silicon photodiode matrix, or direct conversion, using a photoconductive layer of amorphous selenium (a-Se) combined with thin-film transistor array. The goal of this study was to evaluate a methodology for objectively assessing image quality to compare human observer task performance in detecting microcalcification clusters and extended mass-like lesions achieved with different detector types. The proposed assessment methodology uses a novel anthropomorphic breast phantom fabricated with ink-jet printing. In addition to human observer detection performance, standard linear metrics such as modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were also measured to assess image quality. An Analogic Anrad AXS-2430 a-Se detector used in a commercial FFDM/DBT system and a Teledyne Dalsa Xineos-2329 with CMOS pixel readout were evaluated and compared. The DQE of each detector was similar over a range of exposures. Similar task performance in detecting microcalcifications and masses was observed between the two detectors over a range of clinically applicable dose levels, with some perplexing differences in the detection of microcalcifications at the lowest dose measurement. The evaluation approach presented seems promising as a new technique for objective assessment of breast imaging technology.

Hydroxyapatite mineral enhances malignant potential in a tissue-engineered model of ductal carcinoma in situ (DCIS)

While ductal carcinoma in situ (DCIS) is known as a precursor lesion to most invasive breast carcinomas, the mechanisms underlying this transition remain enigmatic. DCIS is typically diagnosed by the mammographic detection of microcalcifications (MC). MCs consisting of non-stoichiometric hydroxyapatite (HA) mineral are frequently associated with malignant disease, yet it is unclear whether HA can actively promote malignancy. To investigate this outstanding question, we compared phenotypic outcomes of breast cancer cells cultured in control or HA-containing poly(lactide-co-glycolide) (PLG) scaffolds. Exposure to HA mineral in scaffolds increased the expression of pro-tumorigenic interleukin-8 (IL-8) among transformed but not benign cells. Notably, MCF10DCIS.com cells cultured in HA scaffolds adopted morphological changes associated with increased invasiveness and exhibited increased motility that were dependent on IL-8 signaling. Moreover, MCF10DCIS.com xenografts in HA scaffolds displayed evidence of enhanced malignant progression relative to xenografts in control scaffolds. These experimental findings were supported by a pathological analysis of clinical DCIS specimens, which correlated the presence of MCs with increased IL-8 staining and ductal proliferation. Collectively, our work suggests that HA mineral may stimulate malignancy in preinvasive DCIS cells and validate PLG scaffolds as useful tools to study cell-mineral interactions.

Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison

AbstractIn this study, abnormalities in medical images are analysed using three classifiers, and the results are compared. Breast cancer remains a major public health problem among women worldwide. Recently, many algorithms have evolved for the investigation of breast cancer diagnosis through medical imaging. A computer‐aided microcalcification detection method is proposed to categorise the nature of breast cancer as either benign or malignant from input mammogram images. The standard mammogram image corpus, the Mammogram Image Analysis Society database is utilised, and feature extraction is performed using five different wavelet families at level 4 and level 6 decomposition. The work is accomplished through firefly algorithm (FA), extreme learning machine (ELM) and least‐square‐based non‐linear regression (LSNLR) classifiers. The performance of the classifiers is compared by benchmark metrics, such as total error rate, specificity, sensitivity, area under the receiver operating characteristic curve, precision, F1 score and the Matthews correlation coefficient. As validation of the classifier results, a kappa analysis is included to determine the agreement among classifiers. The LSNLR classifier attains a 3% to 7% improvement in average accuracy compared with the average classification accuracy of the FA (86.75%) and ELM (90.836%) classifiers.

A New Imaging Technique for the Diagnosis of Thyroid Cancer: Thyroidography

Background: Microcalcifications are the most important diagnostic parameter for thyroid cancer. We developed a new imaging technique based on low dose X-rays that specifically reveal microcalcifications via a new software algorithm. Method: A prospective clinical trial was planned and 45 cases (37 women, 8 men, women/men: 4.6, age range: 17–68, mean age: 43) were included in the study. After total thyroidectomy with standard technique, the fresh thyroid tissues were integrated into the human neck simulator. Raw (DICOM) images were obtained for each tissue sample with a standard mammography device and transformed into new images (thyroidograms) by a new software algorithm. Preoperative ultrasonography (USG) images, DICOM images, and thyroidograms were evaluated in order to observe micro and macrocalcifications by the same experienced radiologist. Results: Twenty-four cases were malignant and 21 cases were benign after histopathologic evaluation. Microcalcification detection with the thyroidography was statistically significant for sensitivity, positive predictive value, negative predictive value, and accuracy (p < 0.05) but not for specificity (p > 0.05) compared to the USG and DICOM images for the malignant and also benign cases. Conclusion: This new imaging technique is hopeful for diagnosis of thyroid cancer according to microcalcification detection and may complement thyroid USG.

A four-alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet-printed anthropomorphic phantom.

The advent of three-dimensional breast imaging systems such as digital breast tomosynthesis (DBT) has great promise for improving the detection and diagnosis of breast cancer. With these new technologies comes an essential need for testing methods to assess the resultant image quality. Although randomized clinical trials are the gold standard for assessing image quality, phantom-based studies can provide a simpler and less burdensome approach. In this work, a complete framework is presented for task-based evaluation of microcalcification (MCs) detection performance for DBT imaging systems. The framework consists of three parts. The first part is a realistic anthropomorphic physical breast phantom created through inkjet printing, with parchment paper and iodine-doped ink. The second is a method for inserting realistic MCs fabricated from calcium hydroxyapatite. The reproducibility and stability of the phantom materials were investigated through multiple samples of parchment and ink over 6 months. The final part is an analysis using a four-alternative forced choice (4AFC) reader study. To demonstrate the framework, a task-based 4AFC study was conducted using a clinical system to compare performance from DBT, synthetic mammography (SM), and full-field digital mammography (FFDM). Nine human observers read images containing MC clusters imaged with all three modalities and tried to correctly locate the MCs. The proportion correct (PC) was measured as the number of correctly detected clusters out of all trials. Overall, readers scored the highest with FFDM, (PC=0.95±0.03) then DBT (0.85±0.04), and finally SM (0.44±0.06). For the parchment and ink samples, the linear attenuation properties were very stable over 6 months. In addition, little difference was found between the various parchment and ink samples, indicating good reproducibility. This framework presents a promising methodology for evaluating diagnostic task performance of clinical breast DBT systems.

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.