Automatic Detection Of Clustered Microcalcifications Research Articles

Detection of Microcalcifications in Digital Mammograms Using Foveal Method

Objective:Breast cancer represents themost frequently diagnosed cancer in women. In order to reducemortality, early detection of breast cancer is important, because diagnosis is more likely to be successful in the early stages of the disease. On the average, the reader's sensitivity can be increased by 10%with the assistance of computer-aided diagnosis (CAD) system. This paper presents a CAD system for the automatic detection of clustered micro-calcifications in digitized mammograms. Methods: The proposed system consists of three main steps. First, breast region is segmented from original mammogram using contrast property of grey level co-occurrence matrix(GLCM). Second, potential micro-calcification pixels in the mammograms are detected by foveal method. Third, in order to reduce false-positive rate, individual micro-calcifications are detected by a set of 8 features extracted from the potential individual micro-calcification objects. Results: In the result, Specificity and sensitivity are used to evaluate the detection performance of micro-calcifications.(sensitivity : 93.1%, specificity : 87.5%). Conclusion: This study could be a useful method for diagnosis of breast cancer as a CAD system.

Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks

In this paper we propose a new algorithm for the detection of clustered microcalcifications using mathematical morphology and artificial neural networks. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a non-uniform background. Considering each mammogram as a topographic representation, each microcalcification appears as an elevation constituting a regional maximum. Morphological filters are applied, in order to remove: (a) noise and (b) regional maxima that do not correspond to calcifications. Each candidate object is marked as such, using a binary image. The original mammogram is used for the final feature extraction step. For the classification step we employ neural network classifiers. We review the performance of two multi-layer perceptrons (MLP) and two radial basis function neural networks (RBFNN) with different number of hidden nodes. The MLP with ten hidden nodes achieved the best classification score with a true positive detection rate of 94.7% and 0.27 false positives per image.

Relevance vector machine for automatic detection of clustered microcalcifications

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.

Multiplexed wavelet transform technique for detection of microcalcification in digitized mammograms.

Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent-child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.

A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films.

Clusters of microcalcifications in mammograms are an important early sign of breast cancer. This paper presents a computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms. The proposed system consists of two main steps. First, potential microcalcification pixels in the mammograms are segmented out by using mixed features consisting of wavelet features and gray level statistical features, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using a set of 31 features extracted from the potential individual microcalcification objects. The discriminatory power of these features is analyzed using general regression neural networks via sequential forward and sequential backward selection methods. The classifiers used in these two steps are both multilayer feedforward neural networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free-response operating characteristics (FROC) curve is used to evaluate the performance. Results show that the proposed system gives quite satisfactory detection performance. In particular, a 90% mean true positive detection rate is achieved at the cost of 0.5 false positive per image when mixed features are used in the first step and 15 features selected by the sequential backward selection method are used in the second step. However, we must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.

Automatic detection of clustered microcalcifications in digitized mammogram films

The existence of clustered microcalcifications is one of the important early signs of breast cancer. This paper presents an image processing procedure for the automatic detection of clustered microcalcifications in digitized mammograms. In particular, a sensi- tivity range of around one false positive per image is targeted. The proposed method consists of two main steps. First, possible micro- calcification pixels in the mammograms are segmented out using wavelet features or both wavelet features and gray level statistical features, and labeled into potential individual microcalcification ob- jects by their spatial connectivity. Second, individual microcalcifica- tions are detected by using the structure features extracted from the potential microcalcification objects. The classifiers used in these two steps are feedforward neutral networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free response operating character- istics curve is used to evaluate the performance. Results show that the proposed procedure gives quite satisfactory detection perfor- mance. In particular, a 93% mean true positive detection rate is achieved at the price of one false positive per image when both wavelet features and gray level statistical features are used in the first step. © 1999 SPIE and IS&T. (S1017-9909(99)00701-1)