A Web-Based Tool for Semi-Automated Segmentation of Histopathological Images Using Nonlinear Color Classifiers

Lopes, Marcelo Dornbusch; Sobieranski, Antonio Carlos; Wangenheim, Aldo von; Comunello, Eros

doi:10.1109/cbms.2016.30

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The Mahalanobis distance, in both its linear and higherorder polynomial variations, has been shown to produce better results than linear color-metric approaches such as RGB or CIELab, when employed as a customized color-metric in various segmentation algorithms [22,23,24,25,26,27,28].…”

Section: Mahalanobis Distancementioning

confidence: 99%

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Júnior

Monteiro

Ribeiro

et al. 2020

RITA

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In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models

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Section: Mahalanobis Distancementioning

confidence: 99%

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Júnior

Monteiro

Ribeiro

et al. 2020

RITA

Self Cite

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Comparison of Supervised Classifiers and Image Features for Crop Rows Segmentation on Aerial Images

Júnior

Monteiro

Ribeiro

et al. 2020

Applied Artificial Intelligence

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Design Of Tumor Image Data Server Aplication Using ITK Library (Insight Toolkit)

Saimara

2020

Mekintek

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In the medical image processing of brain tumors, it is not only focused on the results of CT Scans and MRIs but also supporting software for analyzing brain tumor diseases. Every medical device requires a high specification computer to analyze the disease and this requires a lot of computers so that it requires a lot of funds plus the efficiency of the time needed in the image processing process. ITK is software that will be used to analyze tumors. In saving funds and time efficiency, a centralized computer system based on a network is needed, namely SOAP web service technology.The data to be used is the result of MRI with DICOM format. The system consists of a client application and a server application, the programming language used is C # (C Sharp). The client will send a slice of brain tumor image data on the server and the server will process the image data by segmenting the image data with the threshold segmentation method that is already available on the ITK software and will display the results of segmentation and brain tumor area on the client. Based on the test results, the area of the brain tumor in a slice of 4.45 cm2 by increasing the threshold value will reduce noise and also affect the area of the brain tumor. In testing the system using a standalone computer, the processing time obtained is 2.12 by testing a network-based system using a web service with one client, the processing time is 4.16 seconds, but the results of the black area obtained from the segmentation process are as large as 28.89 cm2. In the addition of four clients, the time difference is obtained by seconds in progress.

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A Web-Based Tool for Semi-Automated Segmentation of Histopathological Images Using Nonlinear Color Classifiers

Cited by 3 publications

References 17 publications

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Comparison of Supervised Classifiers and Image Features for Crop Rows Segmentation on Aerial Images

Design Of Tumor Image Data Server Aplication Using ITK Library (Insight Toolkit)

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