Automatic detection of circulating tumor cells in darkfield microscopic images of unstained blood using boosting techniques.

Circulating tumor cells (CTCs) are nowadays one of the most promising tumor biomarkers. It is well correlated with overall survival and progression-free survival in breast cancer, as well as in many other types of human cancer. In addition, enumeration and analysis of CTCs could be important for monitoring the response to different therapeutic agents, thus guiding the treatment of cancer patients and offering the promise of a more personalized approach.

Systems of nonlinear algebraic equations with positive solutions.

We are concerned with the positive solutions of an algebraic system depending on a parameter [Formula: see text] and arising in economics. For [Formula: see text] we prove that the system has at least a solution. For [Formula: see text] we give three proofs of the existence and a proof of the uniqueness of the solution.

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases.

A low cost automatic detection and ranging system for space surveillance in the medium Earth orbit region and beyond.

The space around the Earth is filled with man-made objects, which orbit the planet at altitudes ranging from hundreds to tens of thousands of kilometers. Keeping an eye on all objects in Earth's orbit, useful and not useful, operational or not, is known as Space Surveillance. Due to cost considerations, the space surveillance solutions beyond the Low Earth Orbit region are mainly based on optical instruments.

Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge.

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments.