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Positron emission tomography (PET) plays an important role in neurodegenerative disorder diagnosis and neurooncology applications, especially detecting the early metabolism anomalies in human brains. Current lesion detection algorithms can be roughly classified into voxelbased, region of interest (ROI)-based, and global algorithms. These methods may capture the scale and/or location of the lesions...
Positron emission tomography (PET) plays an important role in neurodegenerative disorder diagnosis and neurooncology applications, especially detecting the early metabolism anomalies in human brains. Current lesion detection algorithms can be roughly classified into voxel-based, region of interest (ROI)-based, and global algorithms. These methods may capture the scale and/or location of the lesions...
The multi-view/multi-modal features are commonly used in neuroimaging classification because they could provide complementary information to each other and thus result in better classification performance than single-view features. However, it is very challenging to effectively integrate such rich features, since straightforward concatenation or singleview spectral embedding methods rarely leads to...
PET-CT is now accepted as the best imaging technique for non-invasive staging of lung cancers, and a computer-based abnormality detection is potentially useful to assist the reading physicians in diagnosis. In this paper, we present a new fully-automatic approach to detect abnormalities in the thorax based on global context inference. A max-margin learning-based method is designed to infer the global...
Positron emission tomography — computed tomography (PET-CT) produces co-registered anatomical (CT) and functional (PET) patient information (3D image set) from a single scanning session, and is now accepted as the best imaging technique to accurately stage the most common form of primary lung cancer — non-small cell lung cancer (NSCLC). This paper presents a content-based image retrieval (CBIR) method...
Positron emission tomography - computed tomography (PETCT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a content-based image retrieval system for retrieving similar cases from an imaging database as a reference dataset to aid the physicians in PET-CT scan interpretation...
Content-based image retrieval (CBIR) has been an active research area since mid 90's with major focus on feature extraction, due to its significant impact on image retrieval performance. When applying CBIR in the medical domain, different imaging modalities and anatomical regions require different feature extraction methods that integrate some domain-specific knowledge for effective image retrieval...
This paper presents a framework for effective and fast content-based image retrieval for multi-modality PET-CT lung scans. PET-CT scans present significant advantages in tumor staging, but also place new challenges in computerized image analysis and retrieval. Our framework comprises 5 major components: lung field estimation, texture feature extraction, feature categorization, refinement using SVM,...
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