**1. Introduction**

Cancer is one of the leading causes of death worldwide. In 2008, nearly 13% (7.6 million) of all deaths were cancer related. Among all types of cancer, lung, liver, colon, breast, and cervical are the most frequent ones. Recent studies predict 13.1 million cancer deaths for 2030 [1]. Among women, breast cancer is the deadliest type of cancer. Nearly 1.8% of all worldwide deaths are breast cancer related [2].

Till today, there is no cure for breast cancer, and since the trigger to develop any type of cancer is still a mystery, there is not an effective way to prevent the occurrence. Early detection of breast cancer plays a key role in a positive prognosis. There are several imaging technologies that might be used by specialists for the early detection of breast cancer, such as magnetic resonance imaging, ultrasound, and X-ray mammogram. The last technique is the primary tool used to diagnose and detect breast cancer worldwide, and it has been proved to be the best cost-effective tool to diagnose the disease [3].

In clinical practice, mammography allows for the detection of early signs of tumors before they become apparent [3]. Common signs of early cancer inside the breast tissue are microcalcifications, architectural distortions, and masses [4]. During the screening procedure, radiologists use those signs to generate a standardized evaluation of the risk of cancer in a given patient, called Breast Imaging-Reporting and Data System (BI-RADS). This report helps oncologists to decide a course of action among women at risk of developing breast cancer [5].

The broad use of mammogram has driven the development of computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. While both approaches aim to assist radiologists to detect and diagnose breast cancer as early as possible, CADx systems are used as a second opinion [6] and CADe ones aim to improve visualization of the lesions (with up to 35% improvement in detection rate [7]). However, although it has been shown that CADe systems have helped radiologists to better interpret findings [8], it has also been demonstrated that in some cases they may make interpreting the images more difficult, reducing the accuracy of early cancer detection [7]. Furthermore, these systems also may increase the workload of the radiologists [8].

A typical CADe system, whose workflow is shown in **Figure 1**, consists of two algorithms applied sequentially, one to detect suspicious regions or regions of interest (ROI), and one to refine such regions. The former includes the preprocessing of the images, segmentation of

**Figure 1.** Typical workflow of a CADe/CAD*x* system, adapted from Chen g et al. [13].

the breast tissue, and the detection of the ROI itself. The latter process is performed to reduce the number of false positives [8], and usually relies on machine learning techniques [9–11]. Lastly, the results are presented to the radiologist, highlighting in the original mammography the regions that the analysis deemed highly suspicious. As seen in the same image, CADx systems follow the same workflow as CADe ones. However, besides highlighting areas of higher risk to the radiologist, additional algorithms are used to analyze each ROI and generate a computer-based diagnosis. It is important to mention that, currently, few CADe and CADx commercial systems have been approved by the Food and Drug Administration of the United States of America [12].

**1. Introduction**

110 New Perspectives in Breast Imaging

of the radiologists [8].

deaths are breast cancer related [2].

best cost-effective tool to diagnose the disease [3].

Cancer is one of the leading causes of death worldwide. In 2008, nearly 13% (7.6 million) of all deaths were cancer related. Among all types of cancer, lung, liver, colon, breast, and cervical are the most frequent ones. Recent studies predict 13.1 million cancer deaths for 2030 [1]. Among women, breast cancer is the deadliest type of cancer. Nearly 1.8% of all worldwide

Till today, there is no cure for breast cancer, and since the trigger to develop any type of cancer is still a mystery, there is not an effective way to prevent the occurrence. Early detection of breast cancer plays a key role in a positive prognosis. There are several imaging technologies that might be used by specialists for the early detection of breast cancer, such as magnetic resonance imaging, ultrasound, and X-ray mammogram. The last technique is the primary tool used to diagnose and detect breast cancer worldwide, and it has been proved to be the

In clinical practice, mammography allows for the detection of early signs of tumors before they become apparent [3]. Common signs of early cancer inside the breast tissue are microcalcifications, architectural distortions, and masses [4]. During the screening procedure, radiologists use those signs to generate a standardized evaluation of the risk of cancer in a given patient, called Breast Imaging-Reporting and Data System (BI-RADS). This report helps oncologists to decide a course of action among women at risk of developing breast cancer [5]. The broad use of mammogram has driven the development of computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. While both approaches aim to assist radiologists to detect and diagnose breast cancer as early as possible, CADx systems are used as a second opinion [6] and CADe ones aim to improve visualization of the lesions (with up to 35% improvement in detection rate [7]). However, although it has been shown that CADe systems have helped radiologists to better interpret findings [8], it has also been demonstrated that in some cases they may make interpreting the images more difficult, reducing the accuracy of early cancer detection [7]. Furthermore, these systems also may increase the workload

A typical CADe system, whose workflow is shown in **Figure 1**, consists of two algorithms applied sequentially, one to detect suspicious regions or regions of interest (ROI), and one to refine such regions. The former includes the preprocessing of the images, segmentation of

**Figure 1.** Typical workflow of a CADe/CAD*x* system, adapted from Chen g et al. [13].

Many methodologies used by CADx systems analyze only one breast, or even just a subregion of the breast, at a time. That is, they evaluate the left and right breasts as independent objects, unlike radiologists, who analyze images of both breasts simultaneously to evaluate their asymmetry. Radiologists do so because asymmetry is related to early signs of breast cancer (i.e. parenchymal distortion, bright spots, masses, etc.) [14, 15] and it may be used to reduce the rate of false positive detection of masses [16, 17]. Asymmetry can refer to either a longitudinal study, where current and prior mammograms are compared, or a bilateral study, where differences between the left and right breast are analyzed.

A few CADx systems have already tried to incorporate asymmetry studies to enhance diagnosis [14, 18–20]. Some researchers have studied the use of a feature-based asymmetry analysis, where the mammograms are processed individually and the differences between the individual analyses are used as a mean to quantify asymmetry [21]. This approach has also been used to characterize risk factors, such as breast density, and predict near-term breast cancer [14].

Another method that evaluates asymmetry, this one trying to mimic the approach used by radiologists, is the mammogram subtraction. In this approach, differences between mammograms are enhanced by performing a rigid registration (alignment) of the images. However, this methodology was originally employed only in longitudinal studies [22], comparing the same breast at two different times, since the highly heterogeneous and deformable tissue of the breast has hindered the inclusion of subtraction approaches in bilateral asymmetry studies [19].

Miller et al. [23] proposed a technique for the detection of bilateral symmetry using a semiautomated texture-based procedure that segments the glandular tissue, measuring the shape between views, and thus detecting the occurrence of asymmetries. The algorithm obtained an accuracy of 0.867 on a validation dataset of 30 screening mammogram pairs. Later, Miller et al. [24] presented a method for the detection of bilateral asymmetry based on measures of shape, topology, and distribution of brightness. This method was tested on 104 mammogram pairs, yielding a classification accuracy of 0.74.

Lau et al. [25] proposed a method for the detection of breast tumors that extracted measures of brightness, roughness, and directionality, and was based on localized asymmetry. This method was evaluated using 10 pairs of mammograms where asymmetry was a significant factor in the radiologist's diagnosis. A sensitivity of 0.92 was obtained, with 4.9 false positives per mammogram. However, the alignment was tuned manually using control points.

Ferrari et al. [26] characterized asymmetry as variations in oriented textural patterns, obtained using directional filtering with Gabor wavelets at different orientations and scales. Using a database with 80 images resulted in a classification accuracy of up to 0.744.

Rodriguez-Rojas et al. [21] presented a CADx system targeted to detect high-risk cancer patients. To do so, automated breast tissue segmentations were performed on 200 Mexican subjects labeled as either low- or high-risk according to their BI-RADS score. Then, 50 features were extracted, and bilateral differences between mammograms were defined by subtracting corresponding features in both mammograms. Finally, a genetic algorithm selected a predictive combination of features. Using this methodology, they were able to classify low-risk and high-risk cases with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.88 on a 150-fold cross-validation set. The features included in the model were associated with the differences in signal distribution and tissue shape.

In summary, and as presented, most asymmetry detection methods are either feature-based, rely on simple bilateral subtraction techniques [14, 27], or depend on an ROI provided by a radiologist [24, 25]. Thus, in order to efficiently measure asymmetry, a better and automatic registration must be performed [28]. To do so, alignment has been improved by using the nipple as a reference point [29] and by co-registering both breasts using a robust point matching approach [22]. Nevertheless, none of those works include a fully automated bilateral registration. In this chapter, a methodology that incorporates an automatic asymmetry analysis with both a feature-based and a pixel-wise bilateral subtraction into a CADx system is presented.
