Detection Of Microcalcifications Research Articles

How Does the Display Luminance Level Affect Detectability of Breast Microcalcifications and Spiculated Lesions in Digital Breast Tomosynthesis (DBT) Images?

This study evaluates the influence of the calibrated luminance level of medical displays in the detectability of microcalcifications and spiculated lesions in digital breast tomosynthesis images. Four models of medical displays with calibrated maximum and minimum luminance, respectively, ranging from 500 to 1000 cd/m2 and from 0.5 to 1.0 cd/m2, were investigated. Forty-eight studies were selected by a senior radiologist: 16 with microcalcifications, 16 with spiculated lesions, and 16 without lesions. All images were anonymized and blindly evaluated by one senior and two junior radiologists. For each study, lesion presence or absence and localization statements, interpretative difficulty level, and overall quality were reported. Cohen's kappa statistic was computed between monitors and within or between radiologists to estimate the reproducibility in correctly identifying lesions; for multireader-multicase analysis, the weighted jackknife alternative free-response receiver operating characteristic statistical tool was applied. Intraradiologist reproducibility ranged from 0.75 to 1.00. Interreader as well as reader-truth agreement values were >0.80 and higher with the two 1000 cd/m2 luminance displays than with the lower luminance displays for each radiologist. Performances in the detectability of breast lesions were significantly greater with the 1000 cd/m2 luminance displays when compared to the display with the lowest luminance value (P value <0.001). Our findings highlight the role of display luminance level on the accuracy of detecting breast lesions.

Detection of masses and microcalcifications in digital mammogram images using fuzzy logic

Abstract Background Detection of small breast lesions is a challenging task for radiologists. Computer aided detection (CAD) systems are implemented to aid radiologists in detecting masses and microcalcifications. This has the potential to raise the level of sensitivity in breast cancer detection. Objectives To evaluate a new system to detect suggestions of suspicious small lesions. Methods Small samples were extracted from different tissue types. Texture features were calculated, and the best features were selected using Waikato Environment for Knowledge Analysis (WEKA) software. Subsequently, 7 selected features were used to form a decision tree. To reduce false negative cases, fuzzy logic was used. In the implementation phase, input images were divided into 8 pixel ´ 8 pixel tiles. For each tile, all selected features were computed as fuzzy inputs. Results To evaluate the technique, the suggested system was applied to 326 images obtained from the National Cancer Society of Malaysia. Based on this application, results showed that the suggested system has an acceptable sensitivity of 85.6% and specificity of 90.7%. Conclusions The fuzzy system is a promising technique for early detection of breast cancer.

Estimating the Accuracy Level Among Individual Detections in Clustered Microcalcifications.

Computerized detection of clustered microcalcifications (MCs) in mammograms often suffers from the occurrence of false positives (FPs), which can vary greatly from case to case. We investigate how to apply statistical estimation to determine the number of FPs that are present in a detected MC lesion. First, we describe the number of true positives (TPs) by a Poisson-binomial probability distribution, wherein a logistic regression model is trained to determine the probability for an individual detected MC to be a TP based on its detector output. Afterward, we model the spatial occurrence of FPs in a lesion area by a spatial point process (SPP), of which the distribution parameters are estimated from the detections in the lesion and its surrounding region. Furthermore, to improve the estimation accuracy, we incorporate the Poisson-binomial distribution of the number of TPs into the SPP model using maximum a posteriori estimation. In the experiments, we demonstrated the proposed approach on the detection results from a set of 188 full-field digital mammography (FFDM) images (95 cases) by three existing MC detectors. The results showed that there was a strong consistency between the estimated and the actual number of TPs (or FPs) for these detectors. When the fraction of FPs in detection was varied from 20% to 50%, both the mean and median values of the estimation error were within 11% of the total number of detected MCs in a lesion. In particular, when the number of FPs increased to as high as 11.38 in a cluster on average, the error was 2.51 in the estimated number of FPs. In addition, lesions estimated to be more accurate in detection were shown to have better classification accuracy (for being malignant or benign) than those estimated to be less accurate.

Design and application of a structured phantom for detection performance comparison between breast tomosynthesis and digital mammography

This paper introduces and applies a structured phantom with inserted target objects for the comparison of detection performance of digital breast tomosynthesis (DBT) against 2D full field digital mammography (FFDM). The phantom consists of a 48 mm thick breast-shaped polymethyl methacrylate (PMMA) container filled with water and PMMA spheres of different diameters. Three-dimensionally (3D) printed spiculated masses (diameter range: 3.8–9.7 mm) and non-spiculated masses (1.6–6.2 mm) along with microcalcifications (90–250 µm) were inserted as targets.Reproducibility of the phantom application was studied on a single system using 30 acquisitions. Next, the phantom was evaluated on five different combined FFDM & DBT systems and target detection was compared for FFDM and DBT modes. Ten phantom images in both FFDM and DBT modes were acquired on these 5 systems using automatic exposure control. Five readers evaluated target detectability. Images were read with the four-alternative forced-choice (4-AFC) paradigm, with always one segment including a target and 3 normal background segments. The percentage of correct responses (PC) was assessed based on 10 trials of each reader for each object type, size and imaging modality. Additionally, detection threshold diameters at 62.5 PC were assessed via non-linear regression fitting of the psychometric curve. The reproducibility study showed no significant differences in PC values. Evaluation of target detection in FFDM showed that microcalcification detection thresholds ranged between 110 and 118 µm and were similar compared to the detection in DBT (range of 106–158 µm). In DBT, detection of both mass types increased significantly (p = 0.0001 and p = 0.0002 for non-spiculated and spiculated masses respectively) compared to FFDM, achieving almost 100% detection for all spiculated mass diameters.In conclusion, a structured phantom with inserted targets was able to show evidence for detectability differences between FFDM and DBT modes for five commercial systems. This phantom has potential for application in task-based assessment at acceptance and commissioning testing of DBT systems.

Clinical Experience and Future of ABUS

Hand held US (HHUS) is very useful in the diagnostic setting such as palpable lump, however lack of reproducibility and operator dependency was known to be drawbacks. Automated breast US (ABUS) is designed to make up the weak point of HHUS and was approved to conduct breast cancer screening as an adjunct to mammography. ABUS is also useful for diagnostic setting as well as for screening setting. However, ABUS has limitation of interventional procedure and application of elastography and these drawbacks will be improved in future. Current clinical use and the anticipated features of the future for ABUS are introduced: ABUS screening, second look ABUS, microcalcifications detection, cancer extent evaluation, ductal abnormality and computer aided detection.

Intelligent System for Detection of Micro-Calcification in Breast Cancer

Recently; medical image mining has become one of the well-recognized research area(s) of machine learning and artificial intelligence techniques have been vastly used in various computer added diagnostic systems. Specifically; breast cancer classification problem is considered as one of the most significant problems. For instance, complex, diverse and heterogamous malignant features of micro-calcification in DICOM (Digital Communication in Medicine) images of mammography are very difficult to classify because the persistence of noise in mammogram images creates lots of confusions for doctors. In order to reduce the chances of misdiagnosis and to discernment the difference between malignant and benign lesions of micro-calcification this paper proposes a system so called “Intelligent System For Detection of Micro-Calcification in Breast Cancer” by considering all above stated problems. Overall our system comprises over three main stages. In first stage, adaptive threshold algorithm is used to reduce the noise, and canny edge detection algorithm is used to detect the edges of every macro or micro classification. In second stage, deginated as feature selection is done by using auto-crop algorithm, which crops all types of calcifications and lesions by proposed algorithm so called CFEDNN (Calcification Feature Extraction Deep Neural Networks) which is designed to avoid the manual ROIs (Region of Interest). Decision model is constructed by using DNN (Deep Neural Networks) and the best classification accuracy is measured as 95.6%.

Early Detection of Microcalcification in Digital Mammograms using Neural Network

Early Detection of Microcalcification in Digital Mammograms using Neural Network

Research development of ultrasound MicroPure imaging in detection of breast microcalcification

Research development of ultrasound MicroPure imaging in detection of breast microcalcification

Performance of Photon-Counting Breast Computed Tomography, Digital Mammography, and Digital Breast Tomosynthesis in Evaluating Breast Specimens

This study compared a novel photon-counting breast computed tomography (pcBCT) system with digital mammography (DM) and digital breast tomosynthesis (DBT) systems. For this reason, surgical specimens were examined with all three techniques and rated by three observers. A total of 30 surgical specimens were investigated with DM, DBT, and pcBCT; the associated images were shown to three experienced radiologists. Findings (22 microcalcifications and 23 mass lesions) were recorded and compared to the results of the pathological examination. Sensitivity and specificity for detection of microcalcifications and lesions were calculated and displayed using receiver operating characteristic curves. Sensitivity for microcalcifications was 82% for DM, 70% for DBT, and 85% for pcBCT. Specificity for microcalcifications was 71% for DM, 75% for DBT, and 83% for pcBCT. Sensitivity for lesions was 45% for DM, 62% for DBT, and 65% for pcBCT. Specificity for lesions was 76% for DM, 62% for DBT, and 76% for pcBCT. pcBCT showed a comparable or superior performance compared to the clinically approved DM and DBT systems. Mass lesion detectability can be increased further by the use of contrast media.

Design of a model observer to evaluate calcification detectability in breast tomosynthesis and application to smoothing prior optimization.

In this work, the authors design and validate a model observer that can detect groups of microcalcifications in a four-alternative forced choice experiment and use it to optimize a smoothing prior for detectability of microcalcifications. A channelized Hotelling observer (CHO) with eight Laguerre-Gauss channels was designed to detect groups of five microcalcifications in a background of acrylic spheres by adding the CHO log-likelihood ratios calculated at the expected locations of the five calcifications. This model observer is then applied to optimize the detectability of the microcalcifications as a function of the smoothing prior. The authors examine the quadratic and total variation (TV) priors, and a combination of both. A selection of these reconstructions was then evaluated by human observers to validate the correct working of the model observer. The authors found a clear maximum for the detectability of microcalcification when using the total variation prior with weight βTV = 35. Detectability only varied over a small range for the quadratic and combined quadratic-TV priors when weight βQ of the quadratic prior was changed by two orders of magnitude. Spearman correlation with human observers was good except for the highest value of β for the quadratic and TV priors. Excluding those, the authors found ρ = 0.93 when comparing detection fractions, and ρ = 0.86 for the fitted detection threshold diameter. The authors successfully designed a model observer that was able to predict human performance over a large range of settings of the smoothing prior, except for the highest values of β which were outside the useful range for good image quality. Since detectability only depends weakly on the strength of the combined prior, it is not possible to pick an optimal smoothness based only on this criterion. On the other hand, such choice can now be made based on other criteria without worrying about calcification detectability.

Evaluation of Binarization Algorithms in Preprocessing of Digital Mammographies

Abstract Digital mammograms are a useful tool for breast cancer detection. The quality of digital mammogram image can have a negative effect on the computer-aided diagnosis system. We investigate the use of automatic image thresholding methods for microcalcification detection in mammographic images. Experimental results on the BI-RADS 4 images dataset confirm the proposed approach.

Computer-Aided Detection of Microcalcifications Using Image Enhancement and Support Vector Classification

Computer-Aided Detection of Microcalcifications Using Image Enhancement and Support Vector Classification

Diagnostic performance of digital breast tomosynthesis with a wide scan angle compared to full-field digital mammography for the detection and characterization of microcalcifications

ObjectivesTo assess the diagnostic performance of digital breast tomosynthesis (DBT), with a wide scan-angle, compared to full-field digital mammography (FFDM), for the detection and characterization of microcalcifications. MethodsIRB approval was obtained for this retrospective study. We selected 150 FFDM and DBT (50 benign and 50 malignant histologically verified microcalcifications, 50 cases classified as BI-RADS 1). Four radiologists evaluated, in separate sessions and blinded to patients’ history and histology, the presence of microcalcifications. Cases with microcalcifications were assessed for visibility, characteristics, and grade of suspicion using BI-RADS categories. Detection rate and diagnostic performance were calculated. Visibility, lesions’ characteristics and reading time were analysed. ResultsDetection rate and visibility were good for both FFDM and DBT, without intra-reader differences (P=0.510). Inter-reader differences were detected (P<0.018). Only two lesions were not detected by any reader on either FFDM or DBT. Diagnostic performance with DBT was as good as that of FFDM, but a significant inter-reader difference was found (P=0.041). High inter-reader variability in the use of the descriptors was found. Reading time for DBT was almost twice that for FFDM (44 and 25s, respectively). ConclusionWide scan-angle DBT enabled the detection and characterization of microcalcifications with no significant differences from FFDM. Inter-reader variability was seen.

ANN and Adaboost application for automatic detection of microcalcifications in breast cancer

ObjectiveMicrocalcifications or MCs are considered to be the basic symptoms present in mammograms for breast cancer diagnosis. Therefore, the accurate detection of MCs is mandatory for the on-time diagnosis, effective treatment and reduction of mortality rates due to breast cancer. Mammogram analysis and interpretation is a challenging task, and there are many obstructions to the accurate detection of MCs such as small and non-uniform shape and size of the MCs clusters in addition to low contrast quality of MCs as compared to the rest of the tissue. These shortcomings of manual interpretation of MCs raise the need for an automatic detection system to assist radiologists in mammogram analysis. In this study, an automated system has been developed to minimize the manual inference and diagnose breast cancer with good precision. In this paper, we propose a two-fold detection algorithm. In the first stage, all suspicious regions from the mammogram are segmented out. In the next stage, these suspected regions are fed to a classifier which then detects whether the region was normal, benign or malignant. We compared the performance of a Neural Network classifier with Adaboost. ANN classifier shows more sensitivity and specificity but less accuracy as compared to Adaboost for tested images. Overall results show that the developed algorithm is able to achieve high accuracy and efficiency for the detection and diagnosis of breast cancer lesions for images from two different databases used, and also for mammograms obtained from a local hospital. ConclusionThe suggested algorithm was tested for DDSM, MIAS and local database and showed high level of overall accuracy (98.68%) and sensitivity (80.15%).

A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN

GoalThe micro-calcifications is early symptom of breast cancer, however it is inefficient for radiologists to read mammograms manually. In this study, an automatic detection method for micro-calcification clusters (MCs) in digitized mammograms is proposed. MethodFirstly, the Otsu thresholding method and a minimum enclosing rectangle are used to obtain the breast area. Secondly, we use mathematical morphology and a non-linear transform to enhance contrast, and then bi-orthogonal wavelet is used to extract wavelet high-frequency coefficients. Finally, the MCs are obtained by a modified Simplified Pulse Coupled Neural Network (SPCNN) model. ResultThe system is tested both on the Mammographic Image Analysis Society (MIAS) Database and the database from Japanese Society of Medical Imaging Technology, moreover the clinical database of People's Hospital of Gansu Province is also obtained to verify this proposed method. The detection result shows that this new method is effective both on experiment and clinic. ContributionFirstly, the relationship between the iteration step and the segmentation result is studied to improve the detection rate; secondly, an improved Pulse Coupled Neural Network (PCNN) model without training is proposed to detect MCs, and it is proved to be more practical and effective than the current state-of-the-art models.

Comparison of two psychometric functions in analyzing 4-AFC detection results using a task-based structured phantom for digital mammography

Introduction Detectability of spiculated and non-spiculated mass models and microcalcifications, in a structured background phantom of acrylic spheres and water, was studied for digital mammography via the four-alternative forced-choice (4-AFC) paradigm. Purpose To compare the threshold diameters for mass and microcalcification detection using two types of psychometric curves applied on the contrast-detail data. Material and methods Ten images of the structured phantom were acquired on 23 digital mammography systems under automatic exposure control. The 4-AFC study was performed with in-house developed software and 5 medical physicists as observers. Percentage correctly detected lesions (PC) was obtained for each target type. Two psychometric curves (Weibull and Logistic functions) were applied to determine the threshold diameters and number of detected targets (at PC = 62.5%). Results Both fitting procedures were successfully applied on all systems. After averaging over 23 systems, the threshold diameters for spiculated and non-spiculated masses and microcalcifications, with the Weibull psychometric curves, were 4.0 ± 0.3 mm (R2 = 0.61 ± 0.08), 4.6 ± 0.7 mm (R2 = 0.51 ± 0.11), and 0.114 ± 0.003 mm (R2 = 0.91 ± 0.03) respectively, and the values of 4.0 ± 0.4 mm (R2 = 0.61 ± 0.08), 4.7 ± 0.6 mm (R2 = 0.50 ± 0.11), and 0.114 ± 0.003 mm (R2 = 0.91 ± 0.03) were obtained with the Logistic function. The numbers of detected targets assessed from the two functions were not different (3–4 spiculated masses, 1–2 non-spiculated masses, and 4 groups of microcalcifications). If the PC values were not completely covering the lower and upper than 62.5%, there will be possibility different results occurred from these two functions. Conclusion Weibull and Logistic curve fits gave similar and reproducible threshold diameters for the structured phantom.

Automatised detection of microcalcification in mammography

Introduction An important area in which an improvement of the imaging techniques would be extremely important, is the diagnosis of breast cancer. For this purpose, mammography is the principal diagnostic tool used. Although it is effective in the early detection of breast cancer, exists a real need for new automatic approaches that can improve the accuracy of detection of breast cancer in mammogram Images. In fact, a computerized system as a second reader can support the radiologist in the interpretation of these exams by reducing the number of false positives and thus, the biopsy procedures not necessary. Purpose In this paper, we propose a Computer Aided Detection System (CAD) for the microcalcification in mammogram images as a diagnostic support tool for radiologists in the analysis. Materials and methods We develop a fully automated tool for (1) pre-processing images using the edge detection process described by Canny which was designed to be an optimal edge detector according to particular criteria; (2) region of Interest extraction; (3) Adapted Hough Transform to identify the microcalcification cluster. The proposed method was evaluated using cases from publicly available mammography dataset such as Breast Cancer Digital Repository (BCDR) database. Results We present the results obtained in terms of accuracy, sensitivity, false positive for image. The proposed system shows results comparable state of the art. Conclusion The proposed method was advantageous in the identification of microcalcifications.

Impact of compressed breast thickness and dose on lesion detectability in digital mammography: FROC study with simulated lesions in real mammograms.

The aim of this work was twofold: (1) to examine whether, with standard automatic exposure control (AEC) settings that maintain pixel values in the detector constant, lesion detectability in clinical images decreases as a function of breast thickness and (2) to verify whether a new AEC setup can increase lesion detectability at larger breast thicknesses. Screening patient images, acquired on two identical digital mammography systems, were collected over a period of 2 yr. Mammograms were acquired under standard AEC conditions (part 1) and subsequently with a new AEC setup (part 2), programmed to use the standard AEC settings for compressed breast thicknesses ≤49 mm, while a relative dose increase was applied above this thickness. The images were divided into four thickness groups: T1 ≤ 29 mm, T2 = 30-49 mm, T3 = 50-69 mm, and T4 ≥ 70 mm, with each thickness group containing 130 randomly selected craniocaudal lesion-free images. Two measures of density were obtained for every image: a BI-RADS score and a map of volumetric breast density created with a software application (VolparaDensity, Matakina, NZ). This information was used to select subsets of four images, containing one image from each thickness group, matched to a (global) BI-RADS score and containing a region with the same (local) volpara volumetric density value. One selected lesion (a microcalcification cluster or a mass) was simulated into each of the four images. This process was repeated so that, for a given thickness group, half the images contained a single lesion and half were lesion-free. The lesion templates created and inserted in groups T3 and T4 for the first part of the study were then inserted into the images of thickness groups T3 and T4 acquired with higher dose settings. Finally, all images were visualized using the ViewDEX software and scored by four radiologists performing a free search study. A statistical jackknife-alternative free-response receiver operating characteristic analysis was applied. For part 1, the alternative free-response receiver operating characteristic curves for the four readers were 0.80, 0.65, 0.55 and 0.56 in going from T1 to T4, indicating a decrease in detectability with increasing breast thickness. P-values and the 95% confidence interval showed no significant difference for the T3-T4 comparison (p = 0.78) while all the other differences were significant (p < 0.05). Separate analysis of microcalcification clusters presented the same results while for mass detection, the only significant difference came when comparing T1 to the other thickness groups. Comparing the scores of part 1 and part 2, results for the T3 group acquired with the new AEC setup and T3 group at standard AEC doses were significantly different (p = 0.0004), indicating improved detection. For this group a subanalysis for microcalcification detection gave the same results while no significant difference was found for mass detection. These data using clinical images confirm results found in simple QA tests for many mammography systems that detectability falls as breast thickness increases. Results obtained with the AEC setup for constant detectability above 49 mm showed an increase in lesion detection with compressed breast thickness, bringing detectability of lesions to the same level.

An efficient microcalcifications detection based on dual spatial/spectral processing

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparison with their surrounding tissue. Spatial non linear enhancement can be applied for microcalcification detection. However, efficiency of a such approach depends on breast density: in case of extreme breast density, the contrast between microcalcification's details and their surrounding tissue is attenuated leading to a limitation of spatially based approaches. In that case, frequency analysis such as wavelet based analysis can be more relevant for dissociating microcalcifications. The main goal of Computer Aided Detection systems (CAD) is to detect breast cancer at an early stage for all breast density classes by using entropies to enhance and then detect microcalcification details. Accordingly, we combine our approach a spatial Automatic Non Linear Stretching (ANLS) and Shannon Entropy based Wavelet Coefficient Thresholding (SE_WCT). Validation of the proposed approach is done on the Mammographic Image Analysis Society (MIAS) database. The evaluation of the contrast is based on the Second-Derivative-Like measure of enhancement(SDME). Accordingly, it yields to a mean SDME of 78.8dB on the whole database. The performance metric for evaluating our proposed CAD is the Receiver Operating Characteristic(ROC) curve and the free-response ROC (FROC). An area under the ROC curve Az = 0.92 is obtained as well as 97.14 % of True Positives (TP) with 0,48 False positives per image (FP).

Bilateral occult breast carcinoma presenting as bilateral axillary mass: An unusual presentation

Occult breast cancer presenting as an axillary mass is a rare clinical finding. Less than 5% of breast carcinoma presents as an axillary mass without an obvious primary tumor. Axillary lymph node showing metastatic adenocarcinoma poses diagnostic and therapeutic problems, when it is the only clinical presentation. In a significant proportion of breast cancer cases, mammographic detection of micro-calcifications is indicative of the presence of a breast lesion. We present a rare case of a bilateral occult breast carcinoma in a 40 year female with bilateral axillary mass without any palpable breast mass.