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We have designed a computer aided diagnosis (CADx) system to assess the presence of cancer in FDG PET/CT exams of lymphoma patients. Detection performances of the random decision forest (RDF) and support vector machine (SVM) classifiers were assessed based on a feature set including 115 PET and CT first order and textural parameters. An original feature selection method based on combining different...
Advances in MR technology have improved the potential for visualization of small lesions in brain images. This has resulted in the opportunity to detect cerebral microbleeds (CMBs), small hemorrhages in the brain that are known to be associated with risk of ischemic stroke and intracerebral bleeding. In this paper, we propose a computer aided detection (CAD) system for the detection of CMBs to speed...
This paper presents a method for classification of structural magnetic resonance images (MRI) of the brain. An ensemble of linear support vector machine classifiers (SVMs) is used for classifying a subject as either patient or normal control. Image voxels are first ranked based on the voxel wise t-statistics between the voxel intensity values and class labels. Then voxel subsets are selected based...
We present a texton-based, multi-stage Bayesian level set algorithm which we use to segment colony images of hESC and their derivatives. We extend our previous research segmenting stem cells according to multiresolution texture methods to accommodate colonies and tissues with diffuse and varied textures via a filter bank approach similar to the MR8. Texture features computed for test images are classified...
We segment atherosclerotic plaque components in in-vivo MRI and CT data using supervised voxelwise classification. The most reliable ground truth can be obtained from histology sections, however, it is not straightforward to use this for classifier training as the registration with in-vivo data often shows misalignments. Therefore, for training we incorporate uncertainty in the ground truth via ”soft”...
Histologic imaging plays an important role in discriminating cancerous tissues of several body organs. However, the human histopathological examinations may be subjective and error prone, because of the complexity of the appearances of the histologic texture. These limitations can be overcome by adopting quantitative computational methods with human histopathological examination routines. This study...
White matter lesions (WML) are hyperintense signals in T2-weighted MRI of the brain. Volume and regional distribution of WML have been extensively studied in dementia, but not much attention has been given to texture analysis in these regions. We wanted to explore if it is possible to distinguish patients with dementia from healthy elderly in a classification framework testing different texture features...
The present work presents a new approach, based on the elaboration of a whole video clip of the ecoghraphic acquisition describing the lesion from different viewpoints. The focus of the paper is in comparing performances which can be obtained, using a simple classification algorithm, using the full video clip against the more common single frame approach. The system consists of five modules: preprocessing,...
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,...
In this paper, we propose a new graph-based approach to address the problem of cytological computer-aided diagnostic. Such approach uses our previously introduced formalism of PdEs-based morphology and geometric diffusion on graphs. The approach is illustrated through two applications in cytopathology (involving Feulgen and Papanicolaou colorations), with examples of nucleus extraction and classification.
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...
This study presents a shape and margin characterization method of breast mass lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The overlap between a mass lesion and its minimum volume enclosing ellipsoid (MVEE) is used to capture the overall shape of a lesion. Various statistical measurements on distance from the lesion surface to its MVEE surface are computed to characterize...
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