scholarly journals Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

2005 ◽  
Author(s):  
John Melonakos ◽  
John Melonakos ◽  
Ramsey Al-Hakim ◽  
James Fallon ◽  
Allen Tannenbaum
Keyword(s):  
Prior Knowledge ◽  
Brain Mri ◽  
Image Noise ◽  
Segmentation Algorithm ◽  
Affine Invariant ◽  
Knowledge Based ◽  
Mri Scans ◽  
Schizophrenic Patients ◽  
Anisotropic Smoothing

An Insight Toolkit (ITK) implementation of our knowledgebased segmentation algorithm applied to brain MRI scans is presented in this paper. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayesrule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. We present the results of this code on two different projects. First, we show the effect of applying this code to skull-removed brain MRI scans. Second, we show the effect of applying this code to the extraction of the DLPFC from a user-defined subregion of brain MRI data.We present our results on brain MRI scans, comparing the results of the knowledge-based segmentation to manual segmentations on datasets of schizophrenic patients.

scholarly journals An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter

2006 ◽  
Author(s):  
John Melonakos ◽  
John Melonakos ◽  
Karthik Krishnan ◽  
Allen Tannenbaum
Keyword(s):  
Image Segmentation ◽  
Prior Knowledge ◽  
Brain Mri ◽  
Image Noise ◽  
Segmentation Algorithm ◽  
Bayes Rule ◽  
Affine Invariant ◽  
Bayesian Segmentation ◽  
Mri Scans ◽  
Anisotropic Smoothing

An Insight Toolkit (ITK) filter for image segmentation with applications to brain MRI scans is presented in this paper. Previously, we showed how ITK could be used to implement our algorithm. This paper presents our new ITK filter for Bayesian segmentation along with results on brain MRI scans. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayes rule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. Specifically, we present the implementation of our Bayesian segmentation algorithm applied to brain MRI scans.

Intracranial meningioma in a patient with osteogenesis imperfecta

Open Medicine ◽  
2008 ◽  
Vol 3 (4) ◽  
pp. 517-520
Author(s):  
Parmenion Tsitsopoulos ◽  
Ioannis Anagnostopoulos ◽  
Vasileios Tsitouras ◽  
Ioannis Venizelos ◽  
Philippos Tsitsopoulos
Keyword(s):  
Osteogenesis Imperfecta ◽  
Muscle Power ◽  
Brain Mri ◽  
Intracranial Meningioma ◽  
Type I ◽  
Mri Scans ◽  
Heritable Disorder ◽  
History Of ◽  
One Year ◽  
Gait Disturbances

AbstractOsteogenesis imperfecta (OI) is a heritable disorder characterized mainly by connective tissue manifestations. In dinstinct cases, several neurological features have also been described. A 46-year-old male with a known family history of OI type I presented with progressive gait disturbances and diminished muscle strength. Brain MRI scans revealed an infiltrative intracranial mass occupying both frontoparietal lobes. The patient underwent surgical intervention. The histological diagnosis was an atypical (Grade II) meningioma. The bony parts demonstrated a mixture of osseous defects due to OI and infiltration by the tumor. At one-year follow up the patient′s muscle power partially returned while repeat MRI scans were negative for tumor recurrence.

scholarly journals Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

2017 ◽  
Vol 221 ◽  
pp. 427-436 ◽  
Author(s):  
Anthony L. Schroeder ◽  
Dalma Martinović-Weigelt ◽  
Gerald T. Ankley ◽  
Kathy E. Lee ◽  
Natalia Garcia-Reyero ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
River Basin ◽  
Biological Effects ◽  
Knowledge Based

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

SBAs in Sport, Exercise, and Musculoskeletal Medicine

2021 ◽  
Keyword(s):  
Learning Experience ◽  
Multiple Choice ◽  
Structured Learning ◽  
Subject Matter Experts ◽  
Multiple Choice Questions ◽  
High Quality ◽  
Musculoskeletal Medicine ◽  
Knowledge Based ◽  
Exercise Medicine ◽  
Mri Scans

‘Multiple Choice Questions in Musculoskeletal, Sport & Exercise Medicine’ is a compilation of 400 multiple choice questions (MCQs), where the format is that of single best answer from a choice of five options. The book closely follows the curriculum of the ‘Membership of Faculty of Sport & Exercise Medicine’ (MFSEM) examination, with some questions being clinically oriented and others being knowledge based. This book is not intended to be a substitute for extensive clinical reading but instead to complement the learning process. Questions in this book have been carefully curated by 92 reputable subject matter experts across ten countries and are intended to provide a structured learning experience. The book is comprised of 46 chapters, where the first 23 ask questions and the next 23 provide answers. The answer to each question has a short explanation with a reference, which is intended to stimulate discussion, research and further learning. There is a total of 33 high quality images (MRI scans, plain radiographs, ECGs, ultrasound scans and photographs), 18 tables and 5 diagrams in the book.

scholarly journals Optimal prior knowledge-based direction of arrival estimation

2012 ◽  
Vol 6 (8) ◽  
pp. 731-742 ◽  
Author(s):  
P. Wirfält ◽  
P. Stoica ◽  
G. Bouleux ◽  
M. Jansson
Keyword(s):  
Prior Knowledge ◽  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Knowledge Based

scholarly journals Silent Brain MRI Infarcts and Subsequent Stroke Type In the Cardiovascular health Study

Stroke ◽  
2001 ◽  
Vol 32 (suppl_1) ◽  
pp. 363-363
Author(s):  
Charles B Bernick ◽  
Lewis H Kuller ◽  
Will T Longstreth ◽  
Corinne Dulberg ◽  
Teri A Manolio ◽  
...  
Keyword(s):  
Hemorrhagic Stroke ◽  
Cardiovascular Health ◽  
Brain Mri ◽  
The Elderly ◽  
Health Study ◽  
Cardiovascular Health Study ◽  
Prior History ◽  
Mri Scans ◽  
Cranial Mri ◽  
Silent Mri

P136 Objective: Silent infarcts seen on cranial MRI scans are a risk factor for subsequent clinical stroke in the elderly. This study examines the type of clinical strokes seen in those with silent infarcts. Methods: Cranial MRI examination was completed on 3324 Cardiovascular Health Study (CHS) participants aged 65+ who were without a prior history of clinical stroke. Incident strokes were identified over an average follow-up of 4 years and classified as hemorrhagic or ischemic. Ischemic strokes were further subdivided into lacunar, cardioembolic, atherosclerotic or other/unknown. Results: Silent MRI infarcts >3mm were found in approximately 28% (n=923). Of these, 7% (n=67) subsequently had a clinically evident stroke. The characteristics of the silent MRI infarcts in those who sustained an incident stroke were as folows: 56 had only subcortical infarcts, of which 55 were <20mm; 4 had only cortical infarcts; and 7 had both cortical and subcortical infarcts. Of those with only subcortical silent MRI infarcts, 16% (n=9) went on to a hemorrhagic stroke and 84% (n=47) sustained an ischemic stroke. The ischemic strokes were subtyped as 12 cardioembolic, 3 lacunar, 2 atherosclerotic and 30 unknown/other. Considering only those with cortical silent infarcts, either alone or in combination with subcortical infarcts, there was 1 hemorrhagic stroke and 10 ischemic strokes. Half of the ischemic strokes were cardioembolic and half were unknown type. Conclusion: Elderly individuals with silent subcortical infarcts who go onto subsequent stroke may be at risk not only for lacunar infarcts but also cardioembolic or hemorrhagic strokes.

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