Yuan-Hsiang Chang

Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis.

RATIONALE AND OBJECTIVES We developed and evaluated a computer-aided detection (CAD) scheme for masses in digitized mammograms. METHODS A multistep CAD scheme was developed and tested. The method uses a technique of single-image segmentation with Gaussian bandpass filtering to yield a high sensitivity for mass detection. A rule-based multilayer topographic feature analysis method is then used to classify suspected regions. A set of 260 cases, including 162 verified masses, was divided into two subsets; one set was used to set the rule-based classification and one was used to test the performance of the scheme. RESULTS In a preliminary clinical study, the implemented detection scheme yielded 98% sensitivity with a false-positive detection rate of less than one false-positive region per image. CONCLUSION Single-image segmentation methods seem to have high sensitivity in selecting true-positive mass regions in the first stage of a CAD scheme. A multilayer topographic image feature analysis method in the second stage of a CAD scheme has the potential to significantly reduce the false-positive detection rate.

Computer-aided detection of clustered microcalcifications in digitized mammograms.

RATIONALE AND OBJECTIVES We investigated a computer-aided detection (CAD) scheme for clustered microcalcifications in digitized mammograms. METHODS A multistage CAD scheme was developed and tested. To increase sensitivity, the scheme uses a Gaussian band-pass filter and nonlinear threshold. A multistage local minimum searching routine and a multilayer topographic feature analysis are used to reduce the false-positive detection rate. One hundred ten digitized mammograms were used in this preliminary test, with 55 images containing one or two verified microcalcification clusters. RESULTS The CAD scheme achieved 100% sensitivity and had an average false-positive detection rate of 0.18 per image. CONCLUSION The CAD scheme performs as well as many published schemes and has some unique advantages to further improve detection sensitivity and specificity of future CAD schemes.

Computerized Detection of Masses from Digitized Mammograms: Comparison of Single-Image Segmentation and Bilateral-Image Subtraction

RATIONALE AND OBJECTIVES Two methods--single-image segmentation and bilateral-image subtraction--have been used commonly as the first stage in computer-aided detection (CAD) schemes to detect masses on digitized mammograms. In the current study, we investigated and compared the advantages and disadvantages of the two methods in achieving a high sensitivity for mass detection. METHODS Two CAD schemes were tested. One used Gaussian filtering based on single-image segmentation, and the other used bilateral-image subtraction based on left-right image pairs to identify suspicious mass regions. A clinical database that contained 152 verified mass cases was used to compare the two approaches. RESULTS The single-image segmentation method yielded 100% sensitivity and had a somewhat higher number of initial suspicious regions. The bilateral-image subtraction method missed several true-positive regions at the initial phase. Each approach achieved more than 90% sensitivity at a false-positive rate of approximately 0.8 per image. CONCLUSION Optimal initial image segmentation schemes may depend on the complete detection and classification method used. Single-image segmentation methods may perform comparably with bilateral-image segmentation schemes, and these techniques appear to be more versatile and easily adaptable to future clinical CAD applications.

Comparison of artificial neural network and Bayesian belief network in a computer-assisted diagnosis scheme for mammography

Artificial neural networks (ANN) have been widely used in computer-assisted diagnosis (CAD) schemes as a classification tool to identify abnormalities in digitized mammograms. Because of certain limitations of ANNs, some investigators argue that Bayesian belief network (BBN) may exhibit higher performance. In this study we compared the performance of an ANN and a BBN used in the same CAD scheme. The common databases and the same genetic algorithm (GA) were used to optimize both networks. The experimental results demonstrated that using GA optimization, the performance of the two networks converged to the same level in detecting masses from digitized mammograms. Therefore, in this study we concluded that improving the performance of CAD schemes might be more dependent on optimization of feature selection and diversity of training database than on any particular machine classification paradigm.

Computer-aided detection of clustered microcalcifications on digitized mammograms: A robustness experiment

RATIONALE AND OBJECTIVES The authors assessed the performance of an existing computer-aided diagnosis (CAD) scheme for the detection of clustered microcalcifications in a large image database. METHODS A previously developed, rule-based system was used to assess detectability of microcalcification clusters in a set of 386 digitized mammograms with 239 verified clusters visible on 191 images. The test was performed without any reoptimization of the scheme. None of the 386 images had been used in any previous scheme development or testing procedures. RESULTS The CAD scheme achieved 89.5% sensitivity at an average false-positive detection rate of 0.39 per image. In 75% of all images, no false-positive findings occurred. Twenty-three of 25 false-negative findings (misses) occurred during the last two stages in the detection process. CONCLUSION This scheme produced reasonable results in a large data set of images with a large variety of cluster characteristics.

Computerized localization of breast lesions from two views. An experimental comparison of two methods.

RATIONALE AND OBJECTIVES The authors compared two computerized methods, the arc and cartesian straight-line, for the localization of breast lesions in two mammographic views. METHODS A total of 571 craniocaudal and 571 mediolateral oblique matched mammographic image pairs (or 1142 individual images) depicting 290 pathology-verified masses on both views were selected from our image database. Using a previously developed computer-aided detection scheme, all 290 masses and 3992 suspicious but negative regions were identified. After pairing all identified regions from both views, all masses (true-positive-true-positive matched pairs) and a total of 10330 false-positive pairs (including false-positive-false-positive, true-positive-false-positive, and false-positive-true positive pairs) were assessed as to their position in relation to the nipple using both the arc and the cartesian straight-line methods. Receiver operating characteristic methodology was used to evaluate the performance levels for each method in determining, based solely on location, whether a pair of suspicious regions represented a true mass or a false-positive combination. RESULTS The areas under the receiver operating characteristic curves (Az) were 0.79 and 0.78 for the arc and cartesian straight-line methods, respectively. The difference between the two techniques (as measured by Az) was not statistically significant (P > 0.99). CONCLUSIONS These preliminary results demonstrated that the two methods are comparable in identifying true masses from triangulated observations on two views. However, the arc method is somewhat favorable because only the nipple location is required for localization.

Identification of Clustered Microcalcifications on Digitized Mammograms Using Morphology and Topography-Based Computer-Aided Detection Schemes: A Preliminary Experiment

RATIONALE AND OBJECTIVES A mathematical morphology-based computer-aided detection (CAD) scheme for the identification of clustered microcalcifications was developed and tested. The potential for improving either sensitivity or specificity by combining the results with those previously reported was investigated. METHODS The CAD scheme presented here is based on mathematical morphology and a series of simple rule-based criteria for the identification of clustered microcalcifications. A database of 105 digitized mammograms was used for training and rule setting of the scheme. A test set of 191 digitized mammograms was used to evaluate its performance. The same test set had been used to evaluate a multilayer, topography-based scheme. The results obtained by the two schemes were then combined using logical OR and AND operations. RESULTS The morphology-based and topography-based CAD schemes performed at sensitivities of 82.9% and 89.5%, with false-positive detection rates of 1.3 and 0.4 per image, respectively. A logical OR operation resulted in 95.4% sensitivity. An AND operation achieved 76.2% sensitivity, with no false identifications on 93% of images. CONCLUSIONS By combining the results of the morphology-based and the topography-based schemes, either sensitivity or specificity can be improved.

Incorporation of negative regions in a knowledge-based computer-aided detection scheme

The purpose was to evaluate the effect of incorporating negative but suspicious regions into a knowledge-based computer-aided detection (CAD) scheme of masses depicted in mammograms. To determine if a suspicious region is positive for a mass, the region was compared not only with actually positive regions (masses), but also with known negative regions. A set of quantitative measures (i.e., a positive, a negative, and a combined likelihood measure) was computed. In addition, a process was developed to integrate two likelihood measures that were derived using two selected features. An initial evaluation with 300 positive and 300 negative regions was performed to determine the parameters associated with the likelihood measures. Then, an independent set of 500 positive and 500 negative regions was used to test the performance of the CAD scheme. During the training phase, the performance was improved from Az=0.83 to 0.87 with the incorporation of negative regions and the integration process. During the independent test, the performance was improved from Az=0.80 to 0.83. The incorporation of negative regions and the integration process was found to add information to the scheme. Hence, it may offer a relatively robust solution to differentiate masses from normal tissue in mammograms.

Change of region conspicuity in bilateral mammograms: potential impact on CAD performance

In this study, we test a new method to automatically search for matched regions in bilateral digitized mammograms and to compute differences in region conspicuities in pairs of matched regions. One hundred pairs of bilateral images of the same view were selected for the experiment. Each pair of images depicted one verified mass. These 100 mass regions, along with 356 suspicious but actually negative mass regions, were first detected by a single-image-based CAD scheme. To find the matched regions in the corresponding bilateral images, a Procrustean-type technique was used to register the two images, which corrects the deformation of tissue structure between images by guaranteeing the registration of nipples, skin lines, and chest walls. Then, a region growth algorithm was applied to generate a growth region in the matched area, which has the same effective size as the suspicious region in the abnormal image. The conspicuities in the two matched regions, as well as their differences, were computed. Using the conspicuity in the original mass regions and the difference of conspicuities in the two matched regions as two identification indices to classify this set of 456 suspicious regions, the computed areas under the ROC curves (Az) were 0.77 and 0.75, respectively. This preliminary study indicates that by comparing the difference of conspicuities in two matched regions that a very useful feature for the CAD schemes can be extracted.

Visualization of 3D geometric models of the breast created from contrast-enhanced MRI

Contrast enhanced breast MRI is currently used as an adjuvant modality to x-ray mammography because of its ability to resolve ambiguities and determine the extent of malignancy. This study described techniques to create and visualize 3D geometric models of abnormal breast tissue. MRIs were performed on a General Electric 1.5 Tesla scanner using dual phased array breast coils. Image processing tasks included: 1) correction of image inhomogeneity caused by the coils, 2) segmentation of normal and abnormal tissue, and 3) modeling and visualization of the segmented tissue. The models were visualized using object-based surface rendering which revealed characteristics critical to differentiating benign from malignant tissue. Surface rendering illustrated the enhancement distribution and enhancement patterns. The modeling process condensed the multi-slice MRI data information and standardized its interpretation. Visualizing the 3D models should improve the radiologists and/or surgeons impression of the 3D shape, extent, and accessibility of the malignancy compared to viewing breast MRI data slice by slice.