Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is oftenassociated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcificationsis performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcificationshave been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the senseof adding new features not only related to the shape

Computer-aided discovery of biological activity spectra for anti-aging and anti-cancer olive oil oleuropeins

Aging is associated with common conditions, including cancer, diabetes, cardiovascular disease, and Alzheimer"s disease. The type of multi‐targeted pharmacological approach necessary to address a complex multifaceted disease such as aging might take advantage of pleiotropic natural polyphenols affecting a wide variety of biological processes. We have recently postulated that the secoiridoids oleuropein aglycone (OA) and decarboxymethyl oleuropein aglycone (DOA), two complex polyphenols present in health‐promoting extra virgin olive oil (EVOO), might constitute a new family of plant‐produced gerosuppressant agents. This paper describes an analysis of the biological activity spectra (BAS) of OA and DOA using PASS (Prediction of Activity Spectra for Substances) software. PASS can predict thousands of biological activities, as the BAS of a compound is an intrinsic property that is largely dependent on the compound"s structure and reflects pharmacological effects, physiological and biochemical mechanisms of action, and specific toxicities. Using Pharmaexpert, a tool that analyzes the PASS‐predicted BAS of substances based on thousands of"mechanism‐ effect" and"effect‐mechanism" relationships, we illuminate hypothesis‐generating pharmacological effects, mechanisms of action, and targets that might underlie the anti‐aging/anti‐cancer activities of the gerosuppressant EVOO oleuropeins.

Brain-Computer Interfaces, Virtual Reality, and Videogames

Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.

Brain Computer Interface for Virtual Reality Control

A brain-computer interface (BCI) is a new communication channel between the human brain and a computer. Applications of BCI systems comprise the restoration of movements, communication and environmental control. In this study experiments were made that used the BCI system to control or to navigate in virtual environments (VE) just by thoughts. BCI experiments for navigation in VR were conducted so far with synchronous BCI and asynchronous BCI systems. The synchronous BCI analyzes the EEG patterns in a predefined time window and has 2 to 3 degrees of freedom.