Microcalcifications In Mammographic Images Research Articles

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

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

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.

Using a human visual system model to optimize soft-copy mammography display: influence of MTF compensation

Rationale and Objectives. The investigators developed an efficient method for optimizing cathode ray tube (CRT) monitor performance for digital mammography, based on the correlation between the performance of human observers and the performance of a mathematical computer model of the human visual system. The investigators examined observer performance on soft-copy display of mammographic images that were either unprocessed or processed to compensate for modulation transfer function (MTF) deficiencies in the CRT display. The results were used to validate the human visual system model. Materials and Methods. Six radiologists viewed a series of 250 mammographic images with microcalcification clusters with different contrast levels on a CRT monitor. The images were viewed twice: once without image processing and once with processing designed to compensate for MTF deficiencies in the CRT monitor. The images were analyzed with the JNDmetrix Visual Discrimination Model, which is based on the principles of just-noticeable difference measurement and frequency-channel vision modeling. Receiver operating characteristic (ROC) curves were generated for the human observers and compared statistically with the model observers’ performance. Results. Both human and model performance was better overall with the MTF-compensated images, especially for microcalcifications in the midlevel contrast range. There was a very high correlation between human and model observers. Conclusion. The use of image-processing methods to compensate for limitations in the MTF of CRT monitors can improve the detection performance of radiologists searching for microcalcifications in mammographic images, and a model based on characteristics of the human visual system can be used to predict human observer results accurately.

A fuzzy logic based approach for semiological analysis of microcalcifications in mammographic images

We have developed a new algorithm for the characterization of microcalcification clusters. Fuzzy logic is well suited to represent and to manipulate data and knowledge at different levels of the algorithm. Our algorithm is built in 3 steps: Detection and segmentation of the individual microcalcifications, measurements on the segmented microcalcifications (shape, contrast, relative localization), use of these measurements as inputs of a learning system which concludes if the current case is malignant or not. We first describe some aspects of the fuzzy segmentation we have implemented. Then we explain how we build fuzzy measurements from the segmented objects and how these measurements are manipulated into the fuzzy decision tree we are using. Finally, we present the preliminary results we obtained with our test database.

A fuzzy logic based approach for semiological analysis of microcalcifications in mammographic images

We have developed a new algorithm for the characterization of microcalcification clusters. Fuzzy logic is well suited to represent and to manipulate data and knowledge at different levels of the algorithm. Our algorithm is built in 3 steps: Detection and segmentation of the individual microcalcifications, measurements on the segmented microcalcifications (shape, contrast, relative localization), use of these measurements as inputs of a learning system which concludes if the current case is malignant or not. We first describe some aspects of the fuzzy segmentation we have implemented. Then we explain how we build fuzzy measurements from the segmented objects and how these measurements are manipulated into the fuzzy decision tree we are using. Finally, we present the preliminary results we obtained with our test database. © 1997 John Wiley & Sons, Inc.

A fuzzy logic based approach for semiological analysis of microcalcifications in mammographic images

A fuzzy logic based approach for semiological analysis of microcalcifications in mammographic images

COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY

Computer-assisted detection of microcalcifications in mammographic images will likely require a multistage algorithm that includes segmentation of possible microcalcifications, pattern recognition techniques to classify the segmented objects, a method to determine if a cluster of calcifications exists, and possibly a method to determine the probability of malignancy. This paper focuses on the first three of these stages, and especially on the classification of segmented local bright spots as either calcification or noncalcification. Seven classifiers (linear and quadratic classifiers, binary decision trees, a standard backpropagation network, 2 dynamic neural networks, and a K-nearest neighbor) are compared. In addition, a postprocessing step is performed on objects identified as calcifications by the classifiers to determine if any clusters of microcalcifications exist. A database of digitized film mammograms is used for training and testing. Detection accuracy of individual and clustered microcalcifications is compared across the seven methods using area under the ROC curve as a figure of merit.