Enfoque Híbrido para la Clasificación de Muestras Histológicas para la Detección de Tumores Cerebrales: Descomposición lineal y Redes Neuronales

2021 ◽  
Author(s):  
◽  
I. A. Cruz-Guerrero
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Image ◽  
Morphological Characteristics ◽  
Spectral Information ◽  
Simple Task ◽  
Average Accuracy ◽  
Linear Unmixing ◽  
Hybrid Classification ◽  
Hybrid Methodology ◽  
Research Show

Hyperspectral imaging has demonstrated its potential to provide information of the chemical composition of tissue and also of its morphological characteristics. However, discerning the presence of a pathology through this information is not a simple task. Because of this, a hybrid methodology is proposed in this work, which combines the identification of characteristic components present in a hyperspectral image from linear unmixing methods, and the ability to distinguish patterns from a neural network. The results of this research show that the proposed method can distinguish a tumor condition from histological brain samples with an average accuracy of 86%. The study demonstrates the potential of hybrid classification methodologies in the analysis of spectral information for the identification of histological samples affected by tumor tissue.

scholarly journals Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

2021 ◽  
Author(s):  
Annalisa Appice ◽  
Angelo Cannarile ◽  
Antonella Falini ◽  
Donato Malerba ◽  
Francesca Mazzia ◽  
...  
Keyword(s):  
Visual Attention ◽  
Hyperspectral Image ◽  
Saliency Detection ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Imaging Analysis ◽  
Analysis Task ◽  
Visual Attention Mechanism ◽  
Land Use Monitoring ◽  
Research Resources

AbstractSaliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.

MORPHOLOGICAL CHARACTERISTICS OF PLEOMORPHIC ADENOMAS OF SALIVARY GLANDS (ANALYSIS OF THE SURGICAL MATERIAL)

2020 ◽  
Vol 73 (11) ◽  
pp. 2339-2344
Author(s):  
Igor S. Brodetskyi ◽  
Olena O. Dyadyk ◽  
Mykhailo S. Myroshnychenko ◽  
Valentina I. Zaritska
Keyword(s):  
Salivary Glands ◽  
Pleomorphic Adenoma ◽  
Tumor Tissue ◽  
Morphological Characteristics ◽  
Structural Diversity ◽  
Absolute Number ◽  
Morphological Features ◽  
Morphometric Study ◽  
Pleomorphic Adenomas ◽  
Active Processes

The aim of the study is to reveal the morphological features of pleomorphic adenomas of the salivary glands during a comprehensive examination of the surgical material. Materials and methods: Surgical material from 30 patients with pleomorphic adenomas of the salivary glands was studied. Microspecimens stained with hematoxylin and eosin were studied, using an Olympus BX-41 microscope (Japan) with subsequent processing with the Olympus DP-soft version 3.1 software, which was used to conduct a morphometric study. By morphometry in the tumor tissue, the specific volumes of the parenchyma and stroma, the thickness of the capsule located between the tumor tissue and the tissue of the salivary gland were determined; the absolute number of vessels in the field of view of the microscope was counted at × 100 magnification. Results: Comprehensive morphological analysis of the surgical material of removed neoplasms of the salivary glands has showed that mesenchymal (15 cases, 50.0%) and mixed (10 cases, 33.3%) variants of pleomorphic adenomas are more common, and less often epithelial variants (5 cases, 16.7%). Pleomorphic adenoma is characterized by a different ratio of the epithelial (parenchymal) and mesenchymal (stromal) components forming this tumor, structural diversity and heterogeneity of the structure of these components, which do not have clear boundaries and are mixed with each other. A characteristic feature of pleomorphic adenoma is also the combination in each case of different types of epithelial cells and the structures that they form, as well as areas of various differentiation of the mesenchymal component. Mesenchymal and mixed variants of pleomorphic adenomas, in comparison with the epithelial variant, are more prone to progression and recurrence, as evidenced by our identified active processes of angiogenesis in tumor tissue, frequent tumor invasion of the capsule, thinning of the capsule or the absence of the capsule, less pronounced infiltration of the capsule by immune cells. Conclusions: The morphological features of mesenchymal, mixed and epithelial variants of pleomorphic adenomas of the salivary glands revealed by the authors should be taken into account by clinicians during choosing the tactics for treating the patient, which will undoubtedly help to reduce the incidence of tumor malignization and its recurrence.

scholarly journals Superpixel Spectral Unmixing for Hyperspectral Image Superresolution Using a Coupled Encoder-Decoder Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shao-lei Zhang ◽  
Guang-yuan Fu ◽  
Hong-qiao Wang ◽  
Yu-qing Zhao
Keyword(s):  
Hyperspectral Image ◽  
Visual Quality ◽  
Spectral Unmixing ◽  
The Other ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Spectral Information ◽  
Multispectral Images ◽  
Self Similarity ◽  
Rank Constraint

In this paper, we propose a novel hyperspectral image superresolution method based on superpixel spectral unmixing using a coupled encoder-decoder network. The hyperspectral image and multispectral images are fused to generate high-resolution hyperspectral images through the spectral unmixing framework with low-rank constraint. Specifically, the endmember and abundance information is extracted via a coupled encoder-decoder network integrating the priori for unmixing. The coupled network consists of two encoders and one shared decoder, where spectral information is preserved through the encoder. The multispectral image is clustered into superpixels to explore self-similarity, and then, the superpixels are unmixed to obtain an abundance matrix. By imposing a low-rank constraint on the abundance matrix, we further improve the superresolution performance. Experiments on the CAVE and Harvard datasets indicate that our superresolution method outperforms the other compared methods in terms of quantitative evaluation and visual quality.

scholarly journals Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution

2020 ◽  
Vol 12 (10) ◽  
pp. 1660 ◽  
Author(s):  
Qiang Li ◽  
Qi Wang ◽  
Xuelong Li
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Super Resolution ◽  
Superior Performance ◽  
Spectral Information ◽  
Great Success ◽  
Convolutional Network ◽  
Training Time ◽  
Image Super Resolution

Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.

scholarly journals Hyperspectral Image Classification Based on Sparse Superpixel Graph

2021 ◽  
Vol 13 (18) ◽  
pp. 3592
Author(s):  
Yifei Zhao ◽  
Fengqin Yan
Keyword(s):  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Hyperspectral Image Classification ◽  
Computational Burden ◽  
Large Size ◽  
High Classification Accuracy ◽  
Good Classification

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

scholarly journals Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-hang Li ◽  
Xin Tan ◽  
Wei Zhang ◽  
Qing-bin Jiao ◽  
Yu-xing Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Spectral Information ◽  
Convolution Kernel ◽  
Image Correction ◽  
Image Preprocessing ◽  
Segmentation Image

This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1–1 and sample 1–2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%.

scholarly journals Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image

2014 ◽  
Vol XL-8 ◽  
pp. 683-688
Author(s):  
D. B. Bhushan ◽  
R. R. Nidamanuri
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Contextual Information ◽  
Graph Cut ◽  
Spectral Information ◽  
Cut Method ◽  
Multiple Classifier ◽  
The One

Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has the potential to increase the classification accuracy and reliability of the hyperspectral image. However, the MCS extracts only the spectral information from the hyperspectral image and neglects the spatial contextual information. Incorporating spatial contextual information along with spectral information is necessary to obtain smooth classification maps. Our objective of this paper is to design a methodology to fully exploit the spectral and spatial information from the hyperspectral image for land cover classification using MCS and Graph cut (GC) method. The problem is modelled as the energy minimization problem and solved using &amp;alpha;-expansion based graph cut method. Experiments are conducted with two hyperspectral images and the result shows that the proposed MCS based graph cut method produces good quality classification map.

scholarly journals Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information

2020 ◽  
Vol 17 (4) ◽  
pp. 3450-3477
Author(s):  
Haifeng Song ◽  
◽  
Weiwei Yang ◽  
Songsong Dai ◽  
Lei Du ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Spectral Information ◽  
Dual Channel
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