Infrared thermography to quantify the risk of breast cancer.

From the last decades, infrared thermography is quite often associated with things other than clinical medicine. For example, the chemical, automobile, aeronautic industries and civil engineering. However, thermography is where infrared images of the breast are analyzed by board certified thermographers and an abnormal thermogram is reported as the significant risk for the existence of breast tumor (Ng, 2009). Thermography is a painless, noninvasive, no radiation, as well as being cheaper and faster, easier access. The aim of this review was to identify the views of clinicians on the use of thermography for quantifying the risk of breast cancer. We used articles published recently in a reliable database. Thermography has been convicted over the years; it has been labeled by subjective interpretation. Most of the reviewed articles agree that mammography is currently the main examination chosen by doctors for the screening of breast cancer (Acharya et al., 2010; Kennedy et al., 2009). However, several studies have reported promising results for the technique (Wang et al., 2010). Additionally, some authors suggest that thermography is complementary to other diagnostic methods, and that the best strategy for the early detection of breast cancer would be to use them together (Kennedy et al., 2009; Hersh, 2004). The combination of thermal imaging with other tests would increase accuracy, sensitivity and specificity of the evaluation and allow a better quantification of the risk of breast cancer.

Knowledge-based spatiotemporal linear abstraction

We present a theoretical framework and a case study for reusing the same conceptual and computational methodology for both temporal abstraction and linear (unidimensional) space abstraction, in a domain (evaluation of traffic-control actions) significantly different from the one (clinical medicine) in which the method was originally used. The method, known as knowledge-based temporal abstraction, abstracts high-level concepts and patterns from time-stamped raw data using a formal theory of domain-specific temporal-abstraction knowledge. We applied this method, originally used to interpret time-oriented clinical data, to the domain of traffic control, in which the monitoring task requires linear pattern matching along both space and time. First, we reused the method for creation of unidimensional spatial abstractions over highways, given sensor measurements along each highway measured at the same time point. Second, we reused the method to create temporal abstractions of the traffic behavior, for the same space segments, but during consecutive time points. We defined the corresponding temporal-abstraction and spatial-abstraction domain-specific knowledge. Our results suggest that (1) the knowledge-based temporal-abstraction method is reusable over time and unidimensional space as well as over significantly different domains; (2) the method can be generalized into a knowledge-based linear-abstraction method, which solves tasks requiring abstraction of data along any linear distance measure; and (3) a spatiotemporal-abstraction method can be assembled from two copies of the generalized method and a spatial-decomposition mechanism, and is applicable to tasks requiring abstraction of time-oriented data into meaningful spatiotemporal patterns over a linear, decomposable space, such as traffic over a set of highways.