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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...
Large multimodal datasets such as The Cancer Genome Atlas present an opportunity to perform correlative studies of tissue morphology and genomics to explore the morphological phenotypes associated with gene expression and genetic alterations. In this paper we present an investigation of Cancer Genome Atlas data that correlates morphology with recently discovered molecular subtypes of glioblastoma...
The diagnosis of diffuse gliomas requires the careful inspection of large amounts of visual data. Identifying tissue regions that inform diagnosis is a cumbersome task for human reviewers and is a process prone to inter-reader variability. In this paper we present an automatic method for identifying critical diagnostic regions within whole-slide microscopy images of gliomas. We frame the problem of...
The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating...
Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain images. The segmentation of magnetic resonance image (MRI) is a challenging problem that has received an enormous amount of attention lately. In this paper, we propose a simple and effective segmentation...
Accurate segmentation of magnetic resonance images (MRI) corrupted by intensity inhomogeneity is a challenging problem and has received an enormous amount of attention lately. On the basis of the local image model, we propose a different segmentation method for MR brain images without estimation and correction for intensity heterogeneity. Firstly, we obtain clustering context based on the distributing...
We present a novel use of GPUs (graphics processing units) for the analysis of histopathological images of neuroblastoma, a childhood cancer. Thanks to the advent of modern microscopy scanners, whole-slide histopathological images can now be acquired but the computational costs to analyze these images using sophisticated image analysis algorithms are usually high. In this study, we have implemented...
In this paper, a novel color texture classification approach is introduced and applied to computer-assisted grading of follicular lymphoma from whole-slide tissue samples. The digitized tissue samples of follicular lymphoma were classified into histological grades under a statistical framework. The proposed method classifies the image either into low or high grades based on the amount of cytological...
Biomedical image analysis plays an important role in diagnosing, prognosing, and treating complex diseases. The authors describe a set of techniques for analyzing, processing, and querying large image datasets using semantic and spatial information.
A modified FKCL (MFKCL) algorithm for automatic segmentation of MR brain images is proposed in this paper. This algorithm is an extension of traditional fuzzy Kohonen's competitive learning algorithm. In our method, a factor that can estimate the effect of the neighbor pixels to the central pixel is introduced into the objective function of the standard FKCL algorithm as the local information. The...
The segmentation of magnetic resonance imaging (MRI) with intensity heterogeneity is a challenging problem that has received an enormous amount of attention lately. In this paper, we propose a simple and effective segmentation method called local-based fuzzy clustering (LBFC) for MR brain images that corrupted by intensity heterogeneity. Firstly, a two-tissue-based method (TTBM) is proposed to generate...
Peripheral neuroblastic tumors (pNTs) make the most commonly encountered tumor groups in children. Neuroblastoma, one of the categories in pNTs, is known to have unique biological behaviors with variable clinical prognoses of the patients. Part of the neuroblastoma prognosis is closely related with grade of neuroblastic differentiation. In this work, we present an automatic classification system that...
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