Computer-aided Detection Of Masses Research Articles

Computer-aided Detection of Masses at Mammography: Interactive Decision Support versus Prompts

To compare effectiveness of an interactive computer-aided detection (CAD) system, in which CAD marks and their associated suspiciousness scores remain hidden unless their location is queried by the reader, with the effect of traditional CAD prompts used in current clinical practice for the detection of malignant masses on full-field digital mammograms. The requirement for institutional review board approval was waived for this retrospective observer study. Nine certified screening radiologists and three residents who were trained in breast imaging read 200 studies (63 studies containing at least one screen-detected mass, 17 false-negative studies, 20 false-positive studies, and 100 normal studies) twice, once with CAD prompts and once with interactive CAD. Localized findings were reported and scored by the readers. In the prompted mode, findings were recorded before and after activation of CAD. The partial area under the location receiver operating characteristic (ROC) curve for an interval of low false-positive fractions typical for screening, from 0 to 0.2, was computed for each reader and each mode. Differences in reader performance were analyzed by using software. The average partial area under the location ROC curve with unaided reading was 0.57, and it increased to 0.62 with interactive CAD, while it remained unaffected by prompts. The difference in reader performance for unaided reading versus interactive CAD was statistically significant (P = .009). When used as decision support, interactive use of CAD for malignant masses on mammograms may be more effective than the current use of CAD, which is aimed at the prevention of perceptual oversights.

Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

Image processing algorithms can be used in computer-aided diagnosis systems to extract features directly from digitized mammograms. Typically, two classes of features are extracted from mammograms with these algorithms, namely morphological and non-morphological features. Image texture analysis is an important technique that represents gray level properties of images used to describe non-morphological features. This technique has shown to be a promising technique in analyzing mammographic lesions caused by masses. In this paper, we evaluate texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. In particular, we propose a false positive reduction in computer-aided detection of masses. The data set consisted of 120 cranio-caudal mammograms, half containing a mass, rated as abnormal images, and half with no lesions. The following texture descriptors were then calculated to analyze the regions of interest (ROIs) texture patterns: entropy, energy, sum average, sum variance, and cluster tendency. To select the best set of features for each method, we applied a genetic algorithm (GA). In the ROIs classification stage, we used the Random Forest algorithm, a data mining technique that separates the data into non-overlapping segments. Experimental results showed that the best classification rates were obtained with the wavelet-based feature extraction using GA for selection of the most relevant features, giving an AUC=0.90.

Improving Performance of Computer-aided Detection of Masses by Incorporating Bilateral Mammographic Density Asymmetry: An Assessment

Bilateral mammographic density asymmetry is a promising indicator in assessing risk of having or developing breast cancer. This study aims to assess the performance improvement of a computer-aided detection (CAD) scheme in detecting masses by incorporating bilateral mammographic density asymmetrical information. A testing dataset containing 2400 full-field digital mammograms (FFDM) acquired from 600 examination cases was established. Among them, 300 were positive cases with verified cancer associated with malignant masses and 300 were negative cases. Two computerized schemes were applied to process images of each case. The first single-image based CAD scheme detected suspicious mass regions and the second scheme computed average and difference of mammographic tissue density depicted between the left and right breast. A fusion method based on rotation of the CAD scoring projection reference axis was then applied to combine CAD-generated mass detection scores and either the computed average or difference (asymmetry) of bilateral mammographic density scores. The CAD performance levels with and without incorporating mammographic density information were evaluated and compared using a free-response receiver operating characteristic type data analysis method. CAD achieved a case-based mass detection sensitivity of 0.74 and a region-based sensitivity of 0.56 at a false-positive rate of 0.25 per image. By fusing the CAD and bilateral mammographic density asymmetry scores, the case-based and region-based sensitivity levels of the CAD scheme were increased to 0.84 and 0.69, respectively, at the same false-positive rate. Fusion with average mammographic density only slightly increased CAD sensitivity to 0.75 (case-based) and 0.57 (region-based). This study indicated that 1) bilateral mammographic density asymmetry was a stronger indicator of the case depicting suspicious masses than the average density computed from two breasts and 2) fusion between the conventional CAD scores and bilateral mammographic density asymmetry information could substantially increase CAD performance in mass detection.

The effect of feature selection methods on computer-aided detection of masses in mammograms

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Use of Normal Tissue Context in Computer-Aided Detection of Masses in Mammograms

When reading mammograms, radiologists do not only look at local properties of suspicious regions but also take into account more general contextual information. This suggests that context may be used to improve the performance of computer-aided detection (CAD) of malignant masses in mammograms. In this study, we developed a set of context features that represent suspiciousness of normal tissue in the same case. For each candidate mass region, three normal reference areas were defined in the image at hand. Corresponding areas were also defined in the contralateral image and in different projections. Evaluation of the context features was done using 10-fold cross validation and case based bootstrapping. Free response receiver operating characteristic (FROC) curves were computed for feature sets including context features and a feature set without context. Results show that the mean sensitivity in the interval of 0.05-0.5 false positives/image increased more than 6% when context features were added. This increase was significant ( p < 0.0001). Context computed using multiple views yielded a better performance than using a single view (mean sensitivity increase of 2.9%, p < 0.0001). Besides the importance of using multiple views, results show that best CAD performance was obtained when multiple context features were combined that are based on different reference areas in the mammogram.

Computer-aided detection of masses in full-field digital mammography using screen-film mammograms for training

It would be of great value when available databases of screen-film mammography (SFM) images can be used to train full-field digital mammography (FFDM) computer-aided detection (CAD) systems, as compilation of new databases is costly. In this paper, we investigate this possibility. Firstly, we develop a method that converts an FFDM image into an SFM-like representation. In this conversion method, we establish a relation between exposure and optical density by simulation of an automatic exposure control unit. Secondly, we investigate the effects of using the SFM images as training samples compared to training with FFDM images. Our FFDM database consisted of 266 cases, of which 102 were biopsy-proven malignant masses and 164 normals. The images were acquired with systems of two different manufacturers. We found that, when we trained our FFDM CAD system with a small number of images, training with FFDM images, using a five-fold crossvalidation procedure, outperformed training with SFM images. However, when the full SFM database, consisting of 348 abnormal cases (including 204 priors) and 810 normal cases, was used for training, SFM training outperformed FFDMA training. These results show that an existing CAD system for detection of masses in SFM can be used for FFDM images without retraining.

SU-FF-I-01: 3D Computer-Aided Detection of Masses and Micro-Calcifications From Cone Beam CT Scans: A Breast Phantom Study

Purpose: Cone Beam Breast Computed Tomography (CBBCT) has emerged as a promising new modality for detection and diagnosis of breast cancer. Compared to conventional X‐ray mammography, CBBCT provides much greater detail of breast tissue in three‐dimensions (3D) as well as improved patient comfort. However, these benefits come at the cost of hundreds of image slices for each scan. Here, a validation study was performed using CBBCT data of a breast phantom with known nodules and micro‐calcifications. A novel 3D computer‐aided detection algorithm based on 3D template‐matching was applied to this dataset to evaluate the performance of CAD‐CBBCT. Method and Materials: The CBBCT dataset of the breast phantom consisted of 371 slices with isotropic resolution of 0.27 mm. The central 250 contiguous slices were used in our analysis. The selected imaging volume contained 8 masses with diameter ranging from 0.5–10mm (appearing on 9–37 pixels), and 13 micro‐calcifications with diameter ranging from 0.2–0.5 mm (appearing on 3–5 pixels). Our CAD algorithm models masses and micro‐calcifications as spheres (templates) of varying sizes and searches for structures similar to the templates throughout the entire imaging volume. Templates of 9 different sizes were applied. The end result is a single index (the normalized cross correlation coefficient) for each mass and calcification candidate. An optimal correlation threshold for each template size was then selected to best identify the nodules and micro‐calcifications. Results: 8 out of 8 masses down to 0.5 mm and 12 out of 13 micro‐calcifications down to 0.2 mm were successfully detected, with only 1 false positive finding. The processing time for each template was ∼80 seconds (on a Mac Pro with 2.66GHz CPU, 5G memory). Conclusion: CBBCT and 3D CAD were able to detect in a breast imaging phantom very small nodules and micro‐calcifications with high sensitivity and specificity.