From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

https://academicworks.medicine.hofstra.edu/cgi/viewcontent.cgi?article=7158&context=articles

COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-up

Stroke in Young Patients with Covid-19 Five patients younger than 50 years of age with large-vessel stroke and Covid-19 infection presented to a health system in New York City over a 2-week period....

https://www.nejm.org/doi/pdf/10.1056/NEJMc2009787?articleTools=true

Large-Vessel Stroke as a Presenting Feature of Covid-19 in the Young.

Preliminary clinical data indicate that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with neurological and neuropsychiatric illness. Responding to this, a weekly virtual coronavirus disease 19 (COVID-19) neurology multi-disciplinary meeting was established at the National Hospital, Queen Square, in early March 2020 in order to discuss and begin to understand neurological presentations in patients with suspected COVID-19-related neurological disorders. Detailed clinical and paraclinical data were collected from cases where the diagnosis of COVID-19 was confirmed through RNA PCR, or where the diagnosis was probable/possible according to World Health Organization criteria. Of 43 patients, 29 were SARS-CoV-2 PCR positive and definite, eight probable and six possible. Five major categories emerged: (i) encephalopathies (n = 10) with delirium/psychosis and no distinct MRI or CSF abnormalities, and with 9/10 making a full or partial recovery with supportive care only; (ii) inflammatory CNS syndromes (n = 12) including encephalitis (n = 2, para- or post-infectious), acute disseminated encephalomyelitis (n = 9), with haemorrhage in five, necrosis in one, and myelitis in two, and isolated myelitis (n = 1). Of these, 10 were treated with corticosteroids, and three of these patients also received intravenous immunoglobulin; one made a full recovery, 10 of 12 made a partial recovery, and one patient died; (iii) ischaemic strokes (n = 8) associated with a pro-thrombotic state (four with pulmonary thromboembolism), one of whom died; (iv) peripheral neurological disorders (n = 8), seven with Guillain-Barre syndrome, one with brachial plexopathy, six of eight making a partial and ongoing recovery; and (v) five patients with miscellaneous central disorders who did not fit these categories. SARS-CoV-2 infection is associated with a wide spectrum of neurological syndromes affecting the whole neuraxis, including the cerebral vasculature and, in some cases, responding to immunotherapies. The high incidence of acute disseminated encephalomyelitis, particularly with haemorrhagic change, is striking. This complication was not related to the severity of the respiratory COVID-19 disease. Early recognition, investigation and management of COVID-19-related neurological disease is challenging. Further clinical, neuroradiological, biomarker and neuropathological studies are essential to determine the underlying pathobiological mechanisms that will guide treatment. Longitudinal follow-up studies will be necessary to ascertain the long-term neurological and neuropsychological consequences of this pandemic.

/pdf/the-emerging-spectrum-of-covid-19-neurology-clinical-39z2e4kw6j.pdf

The emerging spectrum of COVID-19 neurology: clinical, radiological and laboratory findings.

Background and purpose COVID-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Apart from respiratory complications, acute cerebrovascular disease (CVD) has been observed in some patients with COVID-19. Therefore, we described the clinical characteristics, laboratory features, treatment and outcomes of CVD complicating SARS-CoV-2 infection. Materials and methods Demographic and clinical characteristics, laboratory findings, treatments and clinical outcomes were collected and analysed. Clinical characteristics and laboratory findings of patients with COVID-19 with or without new-onset CVD were compared. Results Of 219 patients with COVID-19, 10 (4.6%) developed acute ischaemic stroke and 1 (0.5%) had intracerebral haemorrhage. COVID-19 with new onset of CVD were significantly older (75.7±10.8 years vs 52.1±15.3 years, p Conclusion Acute CVD is not uncommon in COVID-19. Our findings suggest that older patients with risk factors are more likely to develop CVD. The development of CVD is an important negative prognostic factor which requires further study to identify optimal management strategy to combat the COVID-19 outbreak.

https://svn.bmj.com/content/svnbmj/5/3/279.full.pdf

Acute Cerebrovascular Disease Following COVID-19: A Single Center, Retrospective, Observational Study

Of the 206 patients who contracted Severe Acute Respiratory Syndrome (SARS) in Singapore five developed large artery cerebral infarctions. Four patients were critically-ill and three died. Intravenous immunoglobulin was given to three patients. An increased incidence of deep venous thrombosis and pulmonary embolism was also observed among the critically-ill patients. We believe our experience warrants an increased vigilance against stroke and other thrombotic complications among critically-ill SARS patients in future outbreaks, especially if treatment such as intravenous immunoglobulin, that increases pro-thrombotic tendency, is contemplated.

/pdf/large-artery-ischaemic-stroke-in-severe-acute-respiratory-4iehghsz8u.pdf

Large artery ischaemic stroke in severe acute respiratory syndrome (SARS).

Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis. This study targets the development of a set of integrated prognostication model combining different classes of outcome and prognostic factors. Methodologies such as discriminant analysis, logistic regression, decision tree, Bayesian network, and neural network were employed in the study. Several prognostication models were developed using prospectively collected data from 513 severe closed head-injured patients admitted to the Neurocritical Unit at National Neuroscience Institute of Singapore, from April 1999 to February 2003. The correlation between prognostic factors at admission and outcome at 6 months following injury was studied. Overfitting error, which may falsely distinguish different outcomes, was compared graphically. Tenfold cross-validation technique, which reduces overfitting error, was used to validate outcome prediction accuracy. The overall prediction accuracy achieved ranged from 49.79% to 81.49%. Consistently high outcome prediction accuracy was seen with logistic regression and decision tree. Combining both logistic regression and decision tree models, a hybrid prediction model was then developed. This hybrid model would more accurately predict the 6-month post-severe head injury outcome using baseline admission parameters.

Hybrid outcome prediction model for severe traumatic brain injury.

Routine spinal navigation for thoraco-lumbar pedicle screw insertion using the O-arm three-dimensional imaging system improves placement accuracy.

Multi-slice computer tomography (CT) scans are widely used in todaypsilas diagnosis of head traumas. It is effective to disclose the bleeding and fractures. In this paper, we present an automated detection of CT scan slices which contain hemorrhages. Our method is robust towards various rotation, displacement and motion blur. Detection of these pathological slices will be useful for further diagnosis and retrieval.

Hemorrhage slices detection in brain CT images

A method for automatic classification of computed tomography (CT) brain images of different head trauma types is presented in this paper. The method has three major steps:1. The images are first segmented to find potential hemorrhage regions using ellipse fitting, background removal and wavelet decomposition technique; 2. For each region, features (such as area, major axis length, etc.) are extracted; 3. Each extracted feature is classified using machine learning algorithm; the images are then classified based on its component regions' classification. The automatic medical image classification will be useful in building a content-based medical image retrieval system.

/pdf/classification-of-ct-brain-images-of-head-trauma-1sxmf5lfc3.pdf

Boon Chuan Pang

Papers

Large artery ischaemic stroke in severe acute respiratory syndrome (SARS).

Hybrid outcome prediction model for severe traumatic brain injury.

Routine spinal navigation for thoraco-lumbar pedicle screw insertion using the O-arm three-dimensional imaging system improves placement accuracy.

Hemorrhage slices detection in brain CT images

Classification of CT brain images of head trauma