Computer-aided detection and classification of microcalcifications in mammograms: a survey

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, one must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.

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

Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms

In this review paper, it is intended to summarize and compare the methods of automatic detection of microcalcifications in digitized mammograms used in various stages of the Computer Aided Detection systems (CAD). In particular, the pre processing and enhancement, bilateral subtraction techniques, segmentation algorithms, feature extraction, selection and classification, classifiers, Receiver Operating Characteristic (ROC); Free-response Receiver Operating Characteristic (FROC) analysis and their performances are studied and compared.

Automatic Detection of Microcalcification in Mammograms– A Review

Texture-based analysis of clustered microcalcifications detected on mammograms

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)

Automatic detection of clustered microcalcifications in digitized mammogram films

The appearance of clustered microcalcifications in mammogram films is one of the important early signs of breast cancer. This paper presents a new image processing system for the automatic detection of clustered microcalcifications in digitized mammogram films. The detection method uses wavelet features and feed forward neural network to find possible microcalcifications pixels and a set of features to locate individual microcalcifications. The proposed method contains two main steps. First, possible microcalcifications pixels in the mammograms are segmented out based on wavelet transform by using feed forward neural networks and labelled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcificatons are identified based on the features of these potential microcalcification objects by using a feed forward neural network as the classifier. The method was applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free response operating characteristics (FROC) curve was used to evaluate the performance. Results show that a 90 percent mean detection rate is achieved at the price of 1 false positive per image by using orthogonal wavelet.

Automatic detection of clustered microcalcifications in digital mammograms based on wavelet features and neural network classification

In this paper, we describe an algorithm for enhancement and detection of microcalcifications in digital mammograms The microcalcifications are first enhanced by a undecimated wavelet transform and segmented by a classification based method After spatial features are extracted from the segmented images, a neural network and a Bayes classifier are used to identify clustered microcalcifications We have tested this algorithm on 40 regions-of-interest images extracted from the database provided by the University Hospital of Nijmegen

Enhancement and identification of microcalcifications in mammogram images using wavelets

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Automatic detection of clustered microcalcifications in digitized mammogram films

Automatic detection of clustered microcalcifications in digital mammograms based on wavelet features and neural network classification

Enhancement and identification of microcalcifications in mammogram images using wavelets