Contrast Of Microcalcifications Research Articles

Evaluation of microcalcification contrast in clinical images for digital mammography and synthetic mammography

PurposeThe aim of this work was to compare, in a clinical study, digital mammography and synthetic mammography imaging by evaluating the contrast in microcalcifications of different sizes. MethodsA retrospective review of microcalcifications from 46 patients was undertaken. A Hologic 3-Dimensions mammography system and a HD Combo protocol was used for simultaneous acquisition of the digital and synthetic images. Microcalcifications were classified in accordance with their size, and patient breast images were classified in accordance with their density as adipose, moderately dense and dense. The contrast of the microcalcifications was measured and the contrast ratio between synthetic and digital images was compared. An additional qualitative assessment of the images was presented to correlate the conspicuity of the microcalcifications with the suppression of the structure noise. ResultsMicrocalcifications in adipose background always exhibit a comparable or better contrast on synthetic images, regardless their size. For moderately dense background, synthetic images show a better contrast in 91.2 % of cases for small microcalcifications and in 90.9 % of cases for large microcalcifications. For a dense background, better contrast is seen in 89.5 % of cases for small microcalcifications, and in 85.7 % of cases for large microcalcifications. The contrast ratio increases with increasing breast glandularity. The suppression of structure noise also contributes to the enhancement of microcalcifications in the synthetic images. ConclusionsSynthetic mammography imaging is superior to digital mammography imaging in terms of microcalcification contrast, regardless their size and breast density.

Reduction of scatter in breast CT yields improved microcalcification visibility

The inadequate visibility of microcalcifications—small calcium deposits that cue radiologists to early stages of cancer—is a major limitation in current designs of dedicated breast computed tomography (bCT). This limitation has previously been attributed to the constituent components, spatial resolution, and utilized dose. Scattered radiation has been considered an occurrence with low-frequency impacts that can be compensated for in post-processing. We hypothesized, however, that the acquisition of scattered radiation has a far more detrimental impact on clinically relevant features than has previously been understood. Critically, acquisition of scatter leads to the reduced visibility of microcalcifications. This hypothesis was investigated and supported via mathematical derivations and simulation studies. We conducted a series of comparative studies in which four bCT systems were simulated under iso-dose and iso-resolution conditions, characterizing the dependencies of microcalcification contrast on accumulated scatter. Included among the simulated systems is a novel bCT design—narrow beam bCT (NB-bCT)—that captures nearly zero scatter. We find that current bCT systems suffer from significant levels of scatter. As validated in theory, depending on the system and size of microcalcifications, between 25% and over 70% of contrast resolution is lost due to scatter. The results in NB-bCT, however, provide evidence that by removing scatter build-up in projections, the contrast of microcalcifications in a bCT image is preserved, regardless of their size or location in the breast.

A Hybrid the Nonsubsampled Contourlet Transform and Homomorphic Filtering for Enhancing Mammograms

Mammogram is important for early breast cancer detection. But due to the low contrast of microcalcifications and noise, it is difficult to detect microcalcification. This paper presents a comparative study in digital mammography image enhancement based on three different algorithms: homomorphic filtering, unsharp masking and our proposed methods. This latter use a hybrid method Combining contourlet and homomorphic filtering. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBES) and target-to-background enhancement measure based on entropy (TBEE). The proposed methods were tested with the referents mammography data Base MiniMIAS. Experimental results show that the proposed method improves the visibility of microcalcification.

A Hybrid the Nonsubsampled Contourlet Transform and Homomorphic Filtering for Enhancing Mammograms

Mammogram is important for early breast cancer detection. But due to the low contrast of microcalcifications and noise, it is difficult to detect microcalcification. This paper presents a comparative study in digital mammography image enhancement based on three different algorithms: homomorphic filtering, unsharp masking and our proposed methods. This latter use a hybrid method Combining contourlet and homomorphic filtering. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBES) and target-to-background enhancement measure based on entropy (TBEE). The proposed methods were tested with the referents mammography data Base MiniMIAS. Experimental results show that the proposed method improves the visibility of microcalcification.

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.

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

Proposing a New Velocity Profile for Continuous X-Ray Tube Motion in Digital Breast Tomosynthesis.

In digital breast tomosynthesis (DBT), a 3D image of the breast is generated from x-ray projections at various angles. There are two mechanisms for acquiring projection images in DBT, step-and-shoot motion and continuous tube motion. The benefit of continuous tube motion is shorter scan time and hence less patient motion; the trade-off is focal spot blurring. To minimize focal spot blurring in a system with continuous tube motion, this study proposes a new velocity profile for the x-ray tube during the scan. Unlike existing systems for which the x-ray tube has constant angular velocity, we investigate a smoothly-varying tube velocity that approaches zero during each projection and is larger between projections. With this unique design, the filtered backprojection reconstruction of a sinusoidal test object was calculated, and modulation was determined at various frequencies. It is shown that the newly proposed tube velocity yields increased modulation in the reconstruction relative to a conventional system with continuous tube motion. The modulation in the re-designed system differs minimally from an analogous step-and-shoot system operated with the same scan time. This improvement in image quality was validated with reconstructions of microcalcifications in computer breast phantoms. It is known that continuous tube motion reduces the contrast of microcalcifications relative to step- and-shoot systems; we show that the newly proposed tube motion increases the contrast of microcalcifications compared to conventional continuous tube motion. In conclusion, this work proposes a strategy for optimizing the velocity of tube motion in DBT.

Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images

Computer aided diagnoses can assists radiologists in detecting microcalcification, crucial evidence in mammogram for the early diagnosis of breast cancer. A novel approach is proposed in this paper for early detection of breast cancer by enhancing microcalcification regions in mammogram images using hybrid neuro-fuzzy technique. As a first stage, the mammogram intensities are fuzzified using three linguistic labels. Then, the inference engine of a classical fuzzy system is replaced by a collection of sixteen parallel neural networks and a cascade neural network in order to reduce the computational time for real-time applications. The parallel cascade neural networks are trained using data sets that randomly selected from the original fuzzy decision matrix. Finally, the value of the local mask centre is enhanced after defuzzification the input sets. This work is extensively evaluated using two different types of resources which are Mammographic Image Analysis Society database (MIAS) and University of South Florida (USF) database. As a result, it found to be sensitive in enhancing the microcalcifications regions in mammogram with very little number false positive regions.

Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications

Breast cancer is one of the leading causes of female mortality in the world, and early detection is an important means of reducing the mortality rate. The presence of microcalcification clusters has been considered as a very important indicator of malignant types of breast cancer, and its detection is important to prevent and treat the disease. This paper presents an effective approach, in order to detect microcalcification clusters in digitized mammograms, based on the synergy of image processing and partitional (hard and fuzzy) clustering techniques. Mathematical morphology has been used for image processing, and is used in this work as a first step, with the purpose of enhancing the contrast of microcalcifications. Image segmentation is an important task in the field of image processing, in order to identify regions with the same features. In the second step, we use image segmentation, using three partitional, hard and fuzzy clustering algorithms, such as k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means, in order to make a comparison of the advantages and drawbacks offered by these algorithms, and which should help to improve the detection of microcalcification clusters in digitized mammograms.

Experimentally determined spectral optimization for dedicated breast computed tomography

The current study aimed to experimentally identify the optimal technique factors (x-ray tube potential and added filtration material/thickness) to maximize soft-tissue contrast, microcalcification contrast, and iodine contrast enhancement using cadaveric breast specimens imaged with dedicated breast computed tomography (bCT). Secondarily, the study aimed to evaluate the accuracy of phantom materials as tissue surrogates and to characterize the change in accuracy with varying bCT technique factors. A cadaveric breast specimen was acquired under appropriate approval and scanned using a prototype bCT scanner. Inserted into the specimen were cylindrical inserts of polyethylene, water, iodine contrast medium (iodixanol, 2.5 mg/ml), and calcium hydroxyapatite (100 mg/ml). Six x-ray tube potentials (50, 60, 70, 80, 90, and 100 kVp) and three different filters (0.2 mm Cu, 1.5 mm Al, and 0.2 mm Sn) were tested. For each set of technique factors, the intensity (linear attenuation coefficient) and noise were measured within six regions of interest (ROIs): Glandular tissue, adipose tissue, polyethylene, water, iodine contrast medium, and calcium hydroxyapatite. Dose-normalized contrast to noise ratio (CNRD) was measured for pairwise comparisons among the six ROIs. Regression models were used to estimate the effect of tube potential and added filtration on intensity, noise, and CNRD. Iodine contrast enhancement was maximized using 60 kVp and 0.2 mm Cu. Microcalcification contrast and soft-tissue contrast were maximized at 60 kVp. The 0.2 mm Cu filter achieved significantly higher CNRD for iodine contrast enhancement than the other two filters (p = 0.01), but microcalcification contrast and soft-tissue contrast were similar using the copper and aluminum filters. The average percent difference in linear attenuation coefficient, across all tube potentials, for polyethylene versus adipose tissue was 1.8%, 1.7%, and 1.3% for 0.2 mm Cu, 1.5 mm Al, and 0.2 mm Sn, respectively. For water versus glandular tissue, the average percent difference was 2.7%, 3.9%, and 4.2% for the three filter types. Contrast-enhanced bCT, using injected iodine contrast medium, may be optimized for maximum contrast of enhancing lesions at 60 kVp with 0.2 mm Cu filtration. Soft-tissue contrast and microcalcification contrast may also benefit from lower tube potentials (60 kVp). The linear attenuation coefficients of water and polyethylene slightly overestimate the values of their corresponding tissues, but the reported differences may serve as guidance for dosimetry and quality assurance using tissue equivalent phantoms.

An automatic method to enhance microcalcifications using Normalized Tsallis entropy

This paper presents a new approach to enhance the contrast of microcalcifications in mammograms using a fuzzy algorithm based on Normalized Tsallis entropy. In phase I, image is fuzzified using Gaussian membership function. In Phase II, using the non-uniformity factor calculated from local information, the contrasts of microcalcifications are enhanced while suppressing the background heavily. Some of the previous works are related to Shannon entropy and Tsallis entropy. This is the first time in literature to propose an enhancement algorithm using Normalized Tsallis entropy. Normalized Tsallis entropy has an extra parameter q. Our proposed work is completely automatic and q values are selected empirically. The proposed approach improves the detection process vastly. Without Normalized Tsallis entropy enhancement, detection of MCs is meager 80.21% Tps with 8.1 Fps, whereas after introduction of Normalized Tsallis entropy, the results have surged to 96.25% Tps with 0.803 Fps.

WE‐E‐L100J‐01: Simulation of Tomosynthesis Mammograms with Cone Beam CT Images of Mastectomy Breast Specimens

Purpose: To describe and discuss about a technique to simulate digital mammograms and tomosynthesis images with cone beam CT images of mastectomy breast specimens. Method and Materials: To allow x‐ray images to be simulated, cone beam breast CT images of a mastectomy breast specimen were acquired at 80 kVp and used to generate a 3‐D map of linear attenuation coefficients for mammographic x‐rays. The map was then deformed to generate a new map for a compressed breast. A software re‐projector was developed and used to compute projection images for a stationary detector and a linearly shifted x‐ray source. A special re‐projector was developed to preserve the contrast of microcalcifications (MCs). The resulting images were then used to reconstruct tomosynthesis mammograms using various reconstruction algorithms. Results: We have successively used cone beam CT images of mastectomy breast specimens to generate 3‐D maps of attenuation coefficients to model compressed breasts and to use them to simulate digital mammograms and tomosynthesis images. It was found that MCs clearly visible in cone beam CT images were not visible in regular mammograms but faintly visible in tomosynthesis images. The MCs were clearly visible in both MC enhanced mammograms and the tomosynthesis images reconstructed from them. Conclusions: Cone beam CT images may be used to model a compressed breast for simulating mammograms and tomosynthesis images. Cone beam CT images are superior to regular or tomosynthesis mammograms in depicting MCs within their resolution limit. MC enhanced re‐projection, however, could be useful in displaying the cone beam CT images with the more conventional appearances of regular and tomosynthesis mammograms while maintaining the contrast and visibility of MCs in the CT images.This work was supported in part by a research grant EB00117 from the NIH‐NIBIB and a research grant CA104759 from the NIH‐NCI.

Detection of clustered microcalcifications in small field digital mammography

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.