Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Perforation of the median nerve with an acupuncture needle guided by ultrasound

In an experiment on one of the authors, we used ultrasound to visualise an acupuncture needle completely perforating the median nerve at the acupuncture point PC6. During this procedure only a slight sensation occurred, and no pain. We conclude that, in individual cases, the median nerve might be perforated without causing pain or neurological problems.

Patients' sensation during and after laserneedle versus metal needle treatment

OBJECTIVES: Aim of the study was to evaluate the patients' sensations during and after laserneedle versus metal needle acupuncture. STUDY DESIGN: The prospective study was performed at the gynaecological outpatient department of a University Teaching Hospital of Bern, Switzerland. Thirty female patients per group were included in the study and randomized into laserneedle or metal needle group. All women visited the acupuncture out patient department because of gynaecological disorders. Age of the patients in the metal needle group was 38 years in median (range 18-73 years); mean age was 41+/-13.3. Age in the laserneedle group was 36 years in median (range 16-60 years) and mean age was 39.1+/-12.2. Interventions were laserneedle acupuncture and metal needle acupuncture. Patients answered a questionnaire before, after the first treatment and prior to the second treatment. The questionnaires asked about the patients' knowledge of the various acupuncture methods and their health condition before treatment, their perception of pain, warmth, tiredness and relaxation during or after application of the needles or during or after the treatment. Statistics were performed by Graph Pad InStat 3 for windows. RESULTS: The common metal needle technique was well known by the patients in comparison to the laserneedle method (p<0.0001***). Laserneedle acupuncture is a method which is painless (p<0.0001***), energy inducing and relaxing (p=0.0257*) which leads to a warming sensation (p=0.0009***) during treatment. CONCLUSION: Both methods laserneedle and metal needle acupuncture are valuable methods in achieving relaxation and improvement of gynaecological symptoms. Laserneedle acupuncture is painless and easy to apply which is a valuable reason to support this technique in the future.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.