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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 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...
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...
A novel method for segmentation of brain tissues in MRI (magnetic resonance imaging) images is proposed in this paper. First, we reduce noise using a versatile wavelet-based filter. Subsequently, watershed algorithm is applied to brain tissues as an initial segmenting method. Normally, the result of classical watershed algorithm on grey-scale textured images such as tissue images is over-segmentation...
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