It is widely accepted that digital mammograms or tomosynthesis mammograms [123] are associated with quite limited use in cases of small breast masses within dense breast tissues owing to the obscure visualization of small masses when using this method, whereas ultrasonography (US) [456] has been clinically proven to be capable of detecting masses even in dense breasts and is widely used in Asia for the detection as well as the classification of breast masses. Typically, handheld probes for US scanners have been used for breast cancer screening with conventional US imaging devices having disadvantages such as operator dependence and poor image quality levels. It should also be noted that it usually takes a long time to perform conventional breast ultrasonography for breast cancer screening with some breast regions still left unscanned; thus, recently various studies have been reported regarding the development of the three-dimensional (3D) breast scanning devices to overcome these shortcomings [78].

With 3D US scanners, the entire breast image volume can be acquired easily and repeatedly in a relatively short time practically without non-scanning breast regions [891011]. However, it should be noted that a considerable amount of US image data upon each screening can exhaust radiologists when faced with the diagnosis of even one case. To reduce the required diagnostic effort using 3D US scanning devices, a computer-aided detection (CAD) system can suggest mass candidates to enhance radiologists’ screening speed and accuracy as a preliminary step in the diagnosis process [12131415161718].

Recently, various studies have been reported regarding computer-assisted detection and classification using various methods. Ikedo et al. [4] reported a fully automatic lesion detection technique using the Canny edge detector [19]. Kim et al. [14] reported a computer-aided detection algorithm using the adjusted Otsu threshold and a support vector machine classifier. Yap et al. [20] reported a lesion detection algorithm which uses hybrid filtering, multifractal processing, and a thresholding segmentation method. Moon and his colleagues reported many successful works using textural, speckle and morphological features with ultrasound elasticity images [18] and automated 3D breast ultrasound (ABUS) images [1517].

In this paper, we propose an automatic mass detection algorithm using the Hough transform technique.

The detection results using the Hough transform technique are shown in Table 1. The proposed detection scheme for breast masses using 3D US images was evaluated using our database. In this paper, to estimate the sensitivity (Sens) of the proposed algorithm, the detected region is considered to be a true positive if the overlapped voxel volume between the detected mass candidate and the clinical gold standard exceeds ΔV_c. If we consider the automated breast mass detection results as a second observer, it is often reasonable that the suggestion of some overlapped mass candidates to a clinician is sufficient to help with the early screening of breast cancer by focusing his attention on a region in close proximity to the actual breast mass.

Among 69 benign and 60 malignant breast masses, 64 benign and 60 malignant masses were successfully detected using the criterion of ΔV_c = 590, and the detection sensitivity and specificity were found to be 96.1% and 90.3% at a false positive rate of 0.84, comparable to the results in other studies [41420].

In this paper, we suggest a parallelepiped-type cube as a candidate for breast mass lesions and obtain a FP candidate of a single large cube instead of many FP candidates at the limit of specificity approaches zero. Therefore, the conventional definition of specificity is not appropriate for our mass detection algorithm. To estimate the specificity and detection efficiency of the proposed algorithm using an ROC analysis, we newly define the volumetric specificity (Spec_V) as where v_{candidate-gold_standard} is defined as the sum of the mass candidate’s voxels not belonging to the gold standard and v_{whole-gold_standard} is the sum of the voxels of the entire volume not belonging to the gold standard. Using the alternative definition of specificity, we obtain the entire voxel set filled with TP/FP voxels without any TN/FN voxels in the limited case of infinitely many candidates, implying that we identify all of the gold standards of the breast masses. However, it is important to note breast masses clearly regardless of the possibility of the absence of a breast mass, giving the asymptotic results of Spec_V = 0.

By increasing the number of Hough cubes as mass candidates, we obtain the ROC curve for the proposed mass detection algorithm using the 3D Hough transform. As shown in Figure 2, the value of the area (A_z) under the ROC curve is 0.971 with ΔV_c = 590, quite comparable to the results in previous studies [4] and indicating that the proposed algorithm is promising for rapid and efficient breast mass screening. In order to examine the ΔV_c dependence of the ROC curve analysis, we increased ΔV_c to 4720, finding that A_z is not greatly reduced, still showing sensitivity of 100% with regard to malignant breast mass detection.

It is especially notable that the sensitivity of malignant breast mass detection is fairly well enhanced to 100% at 0.62 false positives per case [1420]. This may indicate that an ultrasound volume scanner such as an automated breast ultrasound system using the proposed detection algorithm can provide sufficiently accurate and economically efficient annual breast cancer screening with enhanced detection sensitivity for malignant breast masses.

It is well-known that the US images are associated with subjective interpretations even when experienced radiologists are involved, as well as inconsistent image quality levels and user-dependent reproducibility. An automatic breast mass detection algorithm can help radiologists to enhance their diagnostic accuracy for breast cancer screening. It should be noted that further research regarding the validation with a larger data set is necessary. This will be discussed later in a future study.

In conclusion, an automatic breast mass detection algorithm using the Hough transform technique is proposed for 3D US images and its mass detection efficiency is investigated by conducting a ROC analysis. The algorithm includes masking, subsampling, contrast-adjustment, and median-filter steps as preprocessing steps in order to detect spherical edges using the 3D Hough transform effectively. The 3D Hough transform is very simple, fast, and quite accurate when used to detect well-defined geometric shapes from images compared to the other complicated detection algorithms used elsewhere. The proposed algorithm provides mass detection sensitivity of 96.1% at a rate of 0.84 false positives per case, quite comparable to the outcomes in previous studies, with malignant masses detected with a false positive rate of 0.62.