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. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

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. This paper presents a solution for real-time automatic detection and ranging of space objects of altitudes ranging from below the Medium Earth Orbit up to 40,000 km, based on two low cost observation systems built using commercial cameras and marginally professional telescopes, placed 37 km apart, operating as a large baseline stereovision system. The telescopes are pointed towards any visible region of the sky, and the system is able to automatically calibrate the orientation parameters using automatic matching of reference stars from an online catalog, with a very high tolerance for the initial guess of the sky region and camera orientation. The difference between the left and right image of a synchronized stereo pair is used for automatic detection of the satellite pixels, using an original difference computation algorithm that is capable of high sensitivity and a low false positive rate. The use of stereovision provides a strong means of removing false positives, and avoids the need for prior knowledge of the orbits observed, the system being able to detect at the same time all types of objects that fall within the measurement range and are visible on the image

Systems of nonlinear algebraic equations with positive solutions

Abstract We are concerned with the positive solutions of an algebraic system depending on a parameter α > 0 $\alpha> 0$ and arising in economics. For α > 1 $\alpha> 1$ we prove that the system has at least a solution. For 0 < α < 1 $0<\alpha<1$ we give three proofs of the existence and a proof of the uniqueness of the solution. Brouwer’s theorem and inequalities involving convex functions are essential tools in our proofs

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. In this article, we present a new method that could be used for the automatic detection of CTC in blood, based on the microscopic appearance of unstained cells. The proposed method is based on the evaluation of image characteristics and boosting techniques. A dataset of 263 dark field microscopy images was constructed and used for our tests, containing blood spiked with three different types of tumor cells. An overall sensitivity of 92.87% and a specificity of 99.98% were obtained for the detection of CTC, performances which proved to be comparable to those obtained by human experts

Box-plots of B-mode segmentations:

<p>(a) DCM values for liver tumors with a depth of 8 (Liver_8) and 16 (Liver_16), eye tumors and prostate; (b) Area overlap (AO) values in prostate segmentation of <i>fP-CMC</i> (using two different types of initial labels: <i>init 1</i> and <i>init2</i>) vs. GC-FIS <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100972#pone.0100972-Zouqi1" target="_blank">[12]</a>.</p

Segmentation of US prostate data set.

<p> and rows: initialization labels as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100972#pone.0100972-Zouqi1" target="_blank">[12]</a> (yellow for object and blue for background) and <i>fP-CMC-L1</i> segmentation in red. row: initial labels in yellow and <i>fP-CMC-L2</i> segmentation in red contour. Ground truth is in transparent green area. We refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100972#pone.0100972-Zouqi1" target="_blank">[12]</a> for visual comparison with GC-FIS results.</p

Quantitative evaluation of the estimated fetal head circumference versus biometry provided by radiologists.

<p>Quantitative evaluation of the estimated fetal head circumference versus biometry provided by radiologists.</p

Different types of initialization using basic shapes like lines or ellipses, and free hand initialization.

<p>Yellow and light blue correspond to foreground and background labels respectively.</p