Aprendizado de Máquina Simbólico e Técnicas Fractais Para Caracterizar Rejeição em Biópsia Miocárdica

Carvalho, Volgane Oliveira; Neves, Leandro Alves; Godoy, Moacir Fernandes de; Moreira, Roberto Douglas; Moriel, A R; Murta, Leonardo

doi:10.1007/978-3-642-21198-0_70

Cited by 2 publications

(2 citation statements)

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“…Thus, percolation-based features have become more exploited to quantify images due to the ability to analyse percolating structures and clusters, complementing observations made with other fractal measures such as Fractal Dimension (FD) and Lacunarity (LAC). Percolation has been applied to quantify vascular [3], cardiac [4], and bone [5] images. However, these applications have been limited for quantifying binary or grayscale images.…”

Section: Introductionmentioning

confidence: 99%

Percolation Features: An approach for evaluating fractal properties in colour images

Roberto

Neves²,

Longo³

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Percolation Features: An approach for evaluating fractal properties in colour images

Roberto

Neves²,

Longo³

et al. 2022

Software Impacts

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“…Moreover, even those studies, although few in number, have applied percolation as a quantifier, the results were interesting for tumor angiogenesis detection in vascular images [34]. In myocardial images of heart transplanted, percolation was also able to improve the understanding of the rejection process in each studied group [35]. However, in these works, multidimensional observations for the principles of percolation in colored images have not been considered.…”

Section: Introductionmentioning

confidence: 99%

Features based on the percolation theory for quantification of non-Hodgkin lymphomas

Roberto

Neves

Nascimento

et al. 2017

Computers in Biology and Medicine

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Non-Hodgkin lymphomas are a health problem that affects over 70,000 people per year in the United States alone. The early diagnosis and the identification of this lymphoma are essential for an effective treatment. The classification of non-Hodgkin lymphomas is a task that continues to rank as one of the main challenges faced by hematologists, pathologists, as well as in the producing of computer vision methods due to its inherent complexity. In this paper, we present a new method to quantify and classify tissue samples of non-Hodgkin lymphomas based on the percolation theory. The method consists of associating multiscale and multidimensional approaches in order to divide the image into smaller regions and then verifying color similarity between pixels. A cluster labeling algorithm was applied to each region of interest to obtain the values for the number of clusters, occurrence of percolation and coverage ratio of the largest cluster. The method was tested on different classifiers aiming to differentiate three different groups of non-Hodgkin lymphomas. The obtained results (AUC rates between 0.940 and 0.993) were compared to those provided by methods consolidated in the Literature, which indicates that the percolation theory is a suitable approach for identifying these three classes of non-Hodgkin lymphomas, those being: mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia.

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Aprendizado de Máquina Simbólico e Técnicas Fractais Para Caracterizar Rejeição em Biópsia Miocárdica

Cited by 2 publications

References 0 publications

Percolation Features: An approach for evaluating fractal properties in colour images

Percolation Features: An approach for evaluating fractal properties in colour images

Features based on the percolation theory for quantification of non-Hodgkin lymphomas

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