Blood cell segmentation based on the hybrid algorithm of spectral angle mapping and spectral information divergence

2016 ◽  
Author(s):  
Yingying Xue ◽  
Qian Wang ◽  
Qingli Li ◽  
Mei Zhou ◽  
Hongying Liu
Keyword(s):  
Blood Cell ◽  
Hybrid Algorithm ◽  
Cell Segmentation ◽  
Spectral Information ◽  
Information Divergence ◽  
Spectral Angle

scholarly journals Cartografía del aguacate en el sur del estado de México mediante tratamiento digital de imágenes sentinel-2

2020 ◽  
Vol 11 (4) ◽  
pp. 865-879
Author(s):  
Dulce Karen Figueroa-Figueroa ◽  
Jose Francisco Ramírez Dávila ◽  
Xanat Antonio-Némiga ◽  
Andrés González Huerta
Keyword(s):  
Persea Americana ◽  
Spectral Information ◽  
Spectral Angle Mapper ◽  
Information Divergence ◽  
Spectral Angle ◽  
Sentinel 2

El cultivo de aguacate (Persea americana Mill.) es uno de los más importantes en México, entre los estados con mayor producción se encuentra el Estado de México, que es el tercer estado productor a nivel nacional. Coatepec Harinas y Donato Guerra son dos de los municipios más representativos en lo respectivo a esta actividad; sin embargo, no existe un censo que especifique la superficie del cultivo, por lo que el objetivo de esta investigación fue probar métodos de índices de vegetación, algoritmos spectral angle mapper (SAM) y spectral information divergence (SID) y la combinación de estos en las imágenes del sensor Sentinel-2 para evaluar su desempeño en la identificación de áreas plantadas con el cultivo de aguacate. Los resultados se validaron con una matriz de confusión y la comparación de los datos de referencia de entrenamiento y validación. El algoritmo SID alcanzó una precisión de 97.5% para detectar aguacate, mientras que el tratamiento SAM obtuvo una precisión de 63.1%. La combinación de SID con el índice Anthocyanin Reflectance Index 1 (ARI1), proporcionó un mejor resultado sobre la cartografía de validación regional con 85% de precisión. Otras combinaciones de índices y tratamientos dieron resultados inferiores al 50% de la precisión por lo que no se recomiendan. Esta metodología podría ser probada para la detección de otros cultivos de interés comercial, dado que Sentinel-2 muestra ser una alternativa viable para este tipo de estudios, teniendo una buena resolución espectral, además de ser de fácil acceso y manipulación.

scholarly journals Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification

2020 ◽  
Vol 12 (13) ◽  
pp. 2154 ◽  
Author(s):  
Ke Wang ◽  
Ligang Cheng ◽  
Bin Yong
Keyword(s):  
Hyperspectral Image ◽  
Similarity Measures ◽  
Kernel Functions ◽  
Spectral Information ◽  
Spectral Angle Mapper ◽  
Spectral Similarity ◽  
Information Divergence ◽  
Rbf Kernel ◽  
Hyperspectral Classification ◽  
Spectral Angle

Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.

A BandMax and spectral angle mapper based alogrithm for white blood cell segmentation

2017 ◽  
Author(s):  
Xiyue Hou ◽  
Mei Zhou ◽  
Zhen Sun ◽  
Qingli Li ◽  
Yingying Xue ◽  
...  
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Cell Segmentation ◽  
Spectral Angle Mapper ◽  
Spectral Angle

scholarly journals AVALIAÇÃO DA ACURÁCIA DE ALGORITMOS DE CLASSIFICAÇÃO DE IMAGENS ORBITAIS NA BAÍA DA BABITONGA, NORDESTE DE SANTA CATARINA

2021 ◽  
Vol 14 (6) ◽  
pp. 3577
Author(s):  
Celso Voos Vieira ◽  
Pedro Apolonid Viana
Keyword(s):  
Feature Extraction ◽  
Maximum Likelihood ◽  
Mahalanobis Distance ◽  
Minimum Distance ◽  
Spectral Information ◽  
Classification Algorithms ◽  
Neural Net ◽  
Spectral Angle Mapper ◽  
Information Divergence ◽  
Spectral Angle

O objetivo deste trabalho foi a avaliação da acurácia de algoritmos de classificação do uso e cobertura do solo, quando aplicados a uma imagem orbital de média resolução espacial. Para esse estudo foram utilizadas as bandas espectrais da faixa do visível e infravermelho próximo, do sensor Operational Land Imager – OLI na Baía da Babitonga/SC. Foram propostas nove classes de cobertura do solo, que serviram como controle para testar 11 algoritmos classificadores: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper e Spectral Information Divergence. O classificador Maximum Likelihood foi o que apresentou o melhor desempenho, obtendo um índice Kappa de 0,89 e acurácia global de 95,5%, sendo capaz de distinguir as nove classes de cobertura do solo propostas. Evaluation of the Accuracy of Orbital Image Classification Algorithms in Babitonga Bay, northeast of Santa Catarina A B S T R A C TThe objective of this work was to evaluate the classification algorithms accuracy of the soil use and cover when applied to a spatial mean orbital image. For this study we used the visible and near infrared spectral bands of the Operational Land Imager - OLI sensor in Babitonga Bay / SC. Nine classes of soil cover were proposed, which served as control to test 11 classifier algorithms: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper and Spectral Information Divergence. The Maximum Likelihood classifier presented the best performance, obtaining a Kappa index of 0.89 and a global accuracy of 95.5%, being able to distinguish the nine proposed classes of soil cover.Keywords: Algorithms Accuracy, Babitonga Bay, Orbital image, Remote sensing, Soil Use and Cover. 

Development and study of a method for cell separation during white blood cell segmentation on images of bone marrow preparations in information and measurement systems for diagnostics of acute leukemia

2020 ◽  
pp. 68-72
Author(s):  
V.G. Nikitaev ◽  
A.N. Pronichev ◽  
V.V. Dmitrieva ◽  
E.V. Polyakov ◽  
A.D. Samsonova ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Acute Leukemia ◽  
White Blood Cell ◽  
Cell Separation ◽  
Large Scale ◽  
Bone Marrow Cells ◽  
Automated Analysis ◽  
Cell Segmentation ◽  
Measurement Systems

The issues of using of information and measurement systems based on processing of digital images of microscopic preparations for solving large-scale tasks of automating the diagnosis of acute leukemia are considered. The high density of leukocyte cells in the preparation (hypercellularity) is a feature of microscopic images of bone marrow preparations. It causes the proximity of cells to eachother and their contact with the formation of conglomerates. Measuring of the characteristics of bone marrow cells in such conditions leads to unacceptable errors (more than 50%). The work is devoted to segmentation of contiguous cells in images of bone marrow preparations. A method of cell separation during white blood cell segmentation on images of bone marrow preparations under conditions of hypercellularity of the preparation has been developed. The peculiarity of the proposed method is the use of an approach to segmentation of cell images based on the watershed method with markers. Key stages of the method: the formation of initial markers and builds the lines of watershed, a threshold binarization, shading inside the outline. The parameters of the separation of contiguous cells are determined. The experiment confirmed the effectiveness of the proposed method. The relative segmentation error was 5 %. The use of the proposed method in information and measurement systems of computer microscopy for automated analysis of bone marrow preparations will help to improve the accuracy of diagnosis of acute leukemia.

A novel framework for white blood cell segmentation based on stepwise rules and morphological features

2011 ◽  
Author(s):  
Ja-Won Gim ◽  
Junoh Park ◽  
Ji-Hyeon Lee ◽  
ByoungChul Ko ◽  
Jae-Yeal Nam
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Morphological Features ◽  
Cell Segmentation
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