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Classification performance with sparse representation is largely affected by the low discriminative power of image features. In this study, we propose a new sparse representation model, namely the Boosted Multifold Sparse Representation (BMSR), to improve the classification performance. By dividing the training set into multiple subsets, sparse representation using one subset is used as a weak classifier...
With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance...
In this paper, we propose a novel semi-supervised classification method for four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural and pleural-tail, in low dose computed tomography (LDCT) scans. The proposed method focuses on classifier design by incorporating the knowledge extracted from both training and testing datasets, and contains two stages: (1) bipartite graph construction,...
Classification performance with sparse representation is largely affected by the low discriminative power of image features. In this study, we propose a new sparse representation model, namely the Boosted Multifold Sparse Representation (BMSR), to improve the classification performance. By dividing the training set into multiple subsets, sparse representation using one subset is used as a weak classifier...
Content-based image retrieval has been suggested as an aid to medical diagnosis. Techniques based on standard feature descriptors, however, might not represent optimally the pathological characteristics in medical images. In this paper, we propose a new approach for medical image retrieval based on pathology-centric feature extraction and representation; and patch-based local feature extraction and...
Better utilizing the vast amount of valuable information stored in the medical imaging databases is always an interesting research area, and one way is to retrieve similar images as a reference dataset to assist the diagnosis. Distance metric is a core component in image retrieval; and in this paper, we propose a new learning-based distance metric design, based on regression and classification techniques...
The locations of lung nodules relative to the other lung anatomical structures are important hints of malignant cancers. In this paper, we propose a fully automatic method to identify if a lung nodule is well-circumscribed, juxta-vascular, juxta-pleural or pleural tail in computed tomography (CT) images. First, we design an optimized graph model, introducing new global and region-based energy terms,...
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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