Patch-based Corner Detection for Cervical Vertebrae in X-ray Images

Corners hold vital information about size, shape and morphology of a vertebra in an x-ray image, and recent literature [1, 2] has shown promising performance for detecting vertebral corners using a Hough forest-based architecture. To provide spatial context, this method generates a set of 12 patches around a vertebra and uses a machine learning approach to predict corners of a vertebral body through a voting process. In this paper, we extend this framework in terms of patch generation and prediction methods. During patch generation, the square region of interest has been replaced with data-driven rectangular and trapezoidal region of interest which better aligns the patches to the vertebral body geometry, resulting in more discriminative feature vectors. The corner estimation or the prediction stage has been improved by utilising more efficient voting process using a single kernel density estimation. In addition, advanced and more complex feature vectors are introduced. We also present a thorough evaluation of the framework with different patch generation methods, forest training mechanisms and prediction methods. In order to compare the performance of this framework with a more general method, a novel multi-scale Harris corner detector-based approach is introduced that incorporates a spatial prior through a naive Bayes method. All these methods have been tested on a dataset of 90 X-ray images and achieved an average corner localization error of 2.01 mm, representing a 33% improvement in localisation accuracy compared to the previous state-of-the-art method [2]

From parametricity to conservation laws, via Noether's Theorem

Invariance is of paramount importance in programming languages and in physics. In programming languages, John Reynolds' theory of relational parametricity demonstrates that parametric polymorphic programs are invariant under change of data representation, a property that yields "free" theorems about programs just from their types. In physics, Emmy Noether showed that if the action of a physical system is invariant under change of coordinates, then the physical system has a conserved quantity: a quantity that remains constant for all time. Knowledge of conserved quantities can reveal deep properties of physical systems. For example, the conservation of energy is by Noether's theorem a consequence of a system's invariance under time-shifting. In this paper, we link Reynolds' relational parametricity with Noether's theorem for deriving conserved quantities. We propose an extension of System Fω with new kinds, types and term constants for writing programs that describe classical mechanical systems in terms of their Lagrangians. We show, by constructing a relationally parametric model of our extension of Fω, that relational parametricity is enough to satisfy the hypotheses of Noether's theorem, and so to derive conserved quantities for free, directly from the polymorphic types of Lagrangians expressed in our system

Spectroscopy and carrier dynamics in CdSe self-assembled quantum dots embedded in ZnxCdyMg1−x−ySe

Time-resolved and steady-state photoluminescence,reflectivity, and absorption experiments were performed on CdSequantum dots in ZnxCdyMg1−x−ySe barriers. Studies of the capture times of the photoexcited carriers into the quantum dots and of electron-hole recombination times inside the dots were performed. Photoluminescence rise time yielded capture times from 20 ps to 30 ps. All samples exhibit fast and slow photoluminescence decays, consistent with observing two independent but energetically overlapping decays. The faster relaxation times for the sample emitting in the blue range is 90 ps, whereas for the two samples emitting in the green it is 345 ps and 480 ps. The slower relaxation times for the sample emitting in blue is 310 ps, whereas for the samples emitting in green is 7.5 ns. These results are explained on the basis of the structural differences among the quantum-dot samples

Cervical Vertebral Corner Detection using Haar-like Features and Modified Hough Forest

The neck (cervical spine) is a flexible part of the human body and is particularly vulnerable to injury. Patients suspected of cervical spine injuries are often imaged using lateral view radiographs. Incorrect diagnosis based on these images may lead to serious long-term consequences. Our overarching goal is to develop a computer-aided detection system to help an emergency room physician correctly diagnose a patient's injury. In this paper, we present a method to localize the corners of cervical vertebrae in a set of 90 lateral cervical radiographs. Haar-like features are computed using intensity and gradient image patches, each of which votes for possible corner position using a modified Hough forest regression technique. Votes are aggregated using two dimensional kernel density estimation, to find the location of the corner. Our method demonstrates promising results, identifying corners with an average median error of 2.08 mm

How to Educate Entrepreneurs?

Entrepreneurship education has two purposes: To improve students’ entrepreneurial skills and to provide impetus to those suited to entrepreneurship while discouraging the rest. While entrepreneurship education helps students to make a vocational decision its effects may conflict for those not suited to entrepreneurship. This study shows that vocational and the skill formation effects of entrepreneurship education can be identified empirically by drawing on the Theory of Planned Behavior. This is embedded in a structural equation model which we estimate and test using a robust 2SLS estimator. We find that the attitudinal factors posited by the Theory of Planned Behavior are positively correlated with students’ entrepreneurial intentions. While conflicting effects of vocational and skill directed course content are observed in some individuals, overall these types of content are complements. This finding contradicts previous results in the literature. We reconcile the conflicting findings and discuss implications for the design of entrepreneurship courses

Mixture models for analysis of melting temperature data

<p>Abstract</p> <p>Background</p> <p>In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (T_m) data. However, there is currently no convention on how to statistically analyze such high-resolution T_mdata.</p> <p>Results</p> <p>Mixture model analysis was applied to T_mdata. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in T_mdata obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.</p> <p>Conclusion</p> <p>Mixture model analysis of T_mdata allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows T_mdata to be analyzed, classified, and compared in an unbiased manner.</p

A comparative analysis of algorithms for somatic SNV detection in cancer

Motivation: With the advent of relatively affordable high-throughput technologies, DNA sequencing of cancers is now common practice in cancer research projects and will be increasingly used in clinical practice to inform diagnosis and treatment. Somatic (cancer-only) single nucleotide variants (SNVs) are the simplest class of mutation, yet their identification in DNA sequencing data is confounded by germline polymorphisms, tumour heterogeneity and sequencing and analysis errors. Four recently published algorithms for the detection of somatic SNV sites in matched cancer–normal sequencing datasets are VarScan, SomaticSniper, JointSNVMix and Strelka. In this analysis, we apply these four SNV calling algorithms to cancer–normal Illumina exome sequencing of a chronic myeloid leukaemia (CML) patient. The candidate SNV sites returned by each algorithm are filtered to remove likely false positives, then characterized and compared to investigate the strengths and weaknesses of each SNV calling algorithm. Results: Comparing the candidate SNV sets returned by VarScan, SomaticSniper, JointSNVMix2 and Strelka revealed substantial differences with respect to the number and character of sites returned; the somatic probability scores assigned to the same sites; their susceptibility to various sources of noise; and their sensitivities to low-allelic-fraction candidates.Nicola D. Roberts, R. Daniel Kortschak, Wendy T. Parker, Andreas W. Schreiber, Susan Branford, Hamish S. Scott, Garique Glonek and David L. Adelso

Genome Analysis of the Domestic Dog (Korean Jindo) by Massively Parallel Sequencing

Although pioneering sequencing projects have shed light on the boxer and poodle genomes, a number of challenges need to be met before the sequencing and annotation of the dog genome can be considered complete. Here, we present the DNA sequence of the Jindo dog genome, sequenced to 45-fold average coverage using Illumina massively parallel sequencing technology. A comparison of the sequence to the reference boxer genome led to the identification of 4 675 437 single nucleotide polymorphisms (SNPs, including 3 346 058 novel SNPs), 71 642 indels and 8131 structural variations. Of these, 339 non-synonymous SNPs and 3 indels are located within coding sequences (CDS). In particular, 3 non-synonymous SNPs and a 26-bp deletion occur in the TCOF1 locus, implying that the difference observed in cranial facial morphology between Jindo and boxer dogs might be influenced by those variations. Through the annotation of the Jindo olfactory receptor gene family, we found 2 unique olfactory receptor genes and 236 olfactory receptor genes harbouring non-synonymous homozygous SNPs that are likely to affect smelling capability. In addition, we determined the DNA sequence of the Jindo dog mitochondrial genome and identified Jindo dog-specific mtDNA genotypes. This Jindo genome data upgrade our understanding of dog genomic architecture and will be a very valuable resource for investigating not only dog genetics and genomics but also human and dog disease genetics and comparative genomics