Brain tumor segmentation using extended Weiner and Laplacian lion optimization algorithm with fuzzy weighted k-mean embedding linear discriminant analysis

2022 ◽  
Author(s):  
Surbhi Vijh ◽  
Hari Mohan Pandey ◽  
Prashant Gaurav
Keyword(s):  
Brain Tumor ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Optimization Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Linear Discriminant ◽  
Lion Optimization Algorithm

scholarly journals A Binary Water Wave Optimization Algorithm Applied to Feature Selection

2019 ◽  
Author(s):  
Fagner Macêdo ◽  
Gabriel Barbosa ◽  
Ajalmar Neto
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Optimization Algorithm ◽  
Water Wave ◽  
Principal Component ◽  
Component Analysis ◽  
Linear Discriminant ◽  
Optimum Path

Neste trabalho, o problema de seleção de características é abordado através da introdução de uma nova versão binária para o algoritmo Water Wave Optimization (WWO), chamada Binary Water Wave Optimization (BWWO). O WWO, em sua versão original, é utilizado apenas para resolver problemas de otimização contínuos. O método aqui proposto combina as características de otimização presentes no WWO juntamente com a velocidade de treinamento do algoritmo Optimum-Path Forest (OPF) a fim de providenciar um framework capaz de resolver problemas de seleção de características, que são problemas discretos, de forma eficaz. Para avaliar o desempenho do BWWO, uma análise comparativa é feita com métodos clássicos de redução de dimensionalidade, mais especificamente com Principal Component Analysis (PCA) e Linear Discriminant Analysis (LDA). Com base nos experimentos, pode-se afirmar que BWWO é uma alternativa válida para problemas de seleção de características.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

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