Semantic Image Segmentation Method with Multiple Adjacency Trees and Multiscale Features

Xie, Jiancang; Yu, Lian; Zhu, Lei; Chen, Xiaohong

doi:10.1007/s12559-016-9441-5

Cited by 10 publications

(1 citation statement)

References 35 publications

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“…Classification task implemented using the features of the original image and the Region-Of-Interest (ROI) offered superior result on some image classification problems and this procedure is recommended when the similarity between the normal and the disease class images are more [23,25,30,42,43]. Hence, for the identical images, it is necessary to employ a segmentation technique to extract the ROI from the disease class image with better accuracy [44][45][46]. Finally, the fused features of the actual image and the ROI are fused to attain enhanced classification accuracy.…”

Section: Motivationmentioning

confidence: 99%

Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Dey¹,

Rajinikant²,

Fong³

et al. 2020

Preprint

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The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur’s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.

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