A regional registration technique for automated interval change analysis of breast lesions on mammograms

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Abstract

Analysis of interval change is a useful technique for detection of abnormalities in mammographic interpretation. Interval change analysis is routinely used by radiologists and its importance is well-established in clinical practice. As a first step to develop a computerized method for interval change analysis on mammograms, we are developing an automated regional registration technique to identify corresponding lesions on temporal pairs of mammograms. In this technique, the breast is first segmented from the background on the current and previous mammograms. The breast edges are then aligned using a global alignment procedure based on the mutual information between the breast regions in the two images. Using the nipple location and the breast centroid estimated independently on both mammograms, a polar coordinate system is defined for each image. The polar coordinate of the centroid of a lesion detected on the most recent mammogram is used to obtain an initial estimate of its location on the previous mammogram and to define a fan-shaped search region. A search for a matching structure to the lesion is then performed in the fan-shaped region on the previous mammogram to obtain a final estimate of its location. In this study, a quantitative evaluation of registration accuracy has been performed with a data set of 74 temporal pairs of mammograms and ground-truth correspondence information provided by an experienced radiologist. The most recent mammogram of each temporal pair exhibited a biopsy-proven mass. We have investigated the usefulness of correlation and mutual information as search criteria for determining corresponding regions on mammograms for the biopsy-proven masses. In 85% of the cases (63/74 temporal pairs) the region on the previous mammogram that corresponded to the mass on the current mammogram was correctly identified. The region centroid identified by the registration technique had an average distance of 2.8+/-1.9 mm from the centroid of the radiologist-identified region. These results indicate that our new registration technique may be useful for establishing correspondence between structures on current and previous mammograms. Once such a correspondence is established an interval change analysis could be performed to aid in both detection as well as classification of abnormal breast densities.

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Most cited references 18

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Periodic mammographic follow-up of probably benign lesions: results in 3,184 consecutive cases.

Edward A Sickles (1991)

The author prospectively evaluated the value of periodic mammographic surveillance among 3,184 consecutive cases of nonpalpable, probably benign breast lesions detected with mammography. Follow-up consisted of four mammographic examinations during a 3- or 3.5-year period. Clinical outcome was ascertained in each case after the study period, whether or not patients complied with the protocol. Probably benign lesions were subsequently found to be malignant in 17 cases (positive predictive value for cancer, 0.5%). Fifteen of the 17 cancers were identified by means of interval mammographic change prior to development of a palpable mass; all 17 were stage 0 or stage 1 tumors. All 17 women who had cancer currently show no evidence of tumor recurrence (median duration of follow-up, 5 years). These results should help establish the validity of managing mammographically detected, probably benign lesions with periodic mammographic surveillance. By decreasing the number of biopsies of benign lesions and thereby substantially reducing costs, this approach may help overcome a major barrier to widespread use of mammographic screening.

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Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space.

Jasmine H P Chan, N Petrick, Aguan Wei … (1995)

We studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each ROI. The importance of each feature in distinguishing masses from normal tissue was determined by stepwise linear discriminant analysis. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. We investigated the dependence of classification accuracy on the input features, and on the pixel distance and bit depth in the construction of the SGLD matrices. It was found that five of the texture features were important for the classification. The dependence of classification accuracy on distance and bit depth was weak for distances greater than 12 pixels and bit depths greater than seven bits. By randomly and equally dividing the data set into two groups, the classifier was trained and tested on independent data sets. The classifier achieved an average area under the ROC curve, Az, of 0.84 during training and 0.82 during testing. The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.