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There has been a great interest in the systems that predict clinical labels from the brain images automatically for the last decade since it is a very important task that helps clinicians for decision making. In this study, clinical labels of the structural brain magnetic resonance (MR) images are predicted automatically using the random forests ensemble method. Morphological measurements like volume...
Brain tumor is the abnormal growth of cells or tissues within the brain or surroundings of the brain. MRI is the commonly used modality for the diagnosis of brain tumor. Brain tumor image classification is one of the fundamental problem faced by the clinicians and doctors who worked in this field. In this paper, we proposed a robust method for MR brain image classification based on fractal dimensions...
many methods have been developed for corner detection and matching. However, these detection methods do not take the domain knowledge of brain medical images into account. They produce some useless corners and lose essential domain information. Moreover, existing corner matching methods do not consider the uncertainty and structure of brain medical images. And most of them are developed for 3D medical...
A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various...
Focal cortical dysplasia (FCD) is one of the main causes of epilepsy, and it is of great assistance if the FCD lesions can be localized before the magnetic resonance (MR) imaging guided resective surgery. However, visual detection of these features within the FCD lesional regions is time consuming. - Many automated FCD detection methods have been developed by feature computation, and single classifier...
An automatic segmentation method of hippo-campus for volume measurement in MR brain images by using sparse patch representation and discriminative dictionary learning is proposed in this paper, which can overcome the limitation of multi-atlas approaches that mostly rely on similarity between target image and atlases for more accurate segmentation. In the proposed method, atlases are registered to...
In this paper, we present a new classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from...
Automated MRI segmentation techniques are helpful for a physician for early diagnosis of degenerating diseases in individual patients. Here we are using the T1weighted axial MR images of neuro degenerative diseases. The assessment of the accuracy of the result is done by an expert. FCM an unsupervised clustering technique is implemented in order to classify the brain voxel. The brain voxels are classified...
Medulloblastoma (MB) is the most common brain tumor in children. There are four distinct subtypes of MB, but patients with anaplastic/large cell have the worst prognosis. Since the morbidity is highly correlated with treatment for MB, the ability to distinguish aggressive (such as anaplastic/large cell) MB is crucial. We present a scheme that leverages quantitative image texture features (Haar, Haralick,...
The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research...
We present the first use of multi-region FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre...
A new procedure is proposed for the automated detection of Focal Cortical Dysplasia (FCD) lesions on T1-weighted MRIs using volume-based discriminative features. Statistical features are obtained from of a set of neighboring voxels without using any computation that requires hard labeling of grey matter and white matter tissues. The significance of the proposed features is quantitatively evaluated...
A novel interactive segmentation method based on distance metric learning is proposed for segmentation of tumors in CT and MRI images. Firstly, the moments of the gray-level histogram are extracted as the image features for segmentation. Then, Neighborhood Components Analysis is employed to learn a task-specific distance metric in the feature space using the interactive inputs. The probability of...
Intensity based classification relies on contrast between tissue types adjacent in feature space and adequate signal compared to image noise. Contrast between brain tissue types in Multiple Sclerosis patients Magnetic Resonance Imaging is reduced due to the presence of lesions which intensity values overlap with healthy tissue, resulting in tissue misclassification. We propose a new, extended classifier...
In this paper, we present a comprehensive framework to support classification of nuclei in digital microscopy images of diffuse gliomas. This system integrates multiple modules designed for convenient human annotations, standard-based data management, efficient data query and analysis. In our study, 2770 nuclei of six types are annotated by neuropathologists from 29 whole-slide images of glioma biopsies...
We present a unified approach to Expectation-Maximization (EM) and Level Set image segmentation that combines the advantages of the two algorithms via a geometric prior that encourages local classification similarity. Compared to level sets, our method increases the information returned by providing probabilistic soft decisions, is easily extensible to multiple regions, and does not require solving...
We present the results of a study to determine the sensitivity of biomarkers in in vivo brain MRS signals to post-acquisitional processing algorithms and parameters. Using a comprehensive integrated suite of post-processing and inference algorithms (BIDASCA) we examine the impact of different parameter values for model-based water suppression on the identification of statistically significant wavelet-based...
The feasibility of automating the evaluation of stroke chronic patients' motor functions has been explored while analyzing their corresponding fMRI studies with statistical parametric analysis, statistical inference analysis and a nonlinear multivoxel pattern-analysis classifier based on a feed-forward backward-propagation neural network. After doing principal component analysis and independent component...
Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus...
To discriminate more accurately between autistic and normal brains, we detect the brain cortex variability using a spherical harmonic analysis that represents a 3D surface supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D brain cortex segmentation, with a deformable...
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