Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images

Tenguam, Jaqueline Junko; Rozendo, Guilherme Botazzo; Roberto, Guilherme Freire; Nascimento, Marcelo Zanchetta do; Martins, Alessandro Santana; Neves, Leandro Alves

doi:10.1109/bibm49941.2020.9313575

Cited by 2 publications

(8 citation statements)

References 32 publications

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“…Among the various fractal techniques found in the specialized literature, models from a multidimensional and multiscale perspective, such as fractal dimension (probabilistic [24,41], box-merging [68], and Higuchi [12,28,39]), lacunarity [24,41], and percolation [38] were considered in this proposal, since they allow a complementary quantification of color images. Some image types, such as histological medical images, are spectrally and spatially complex and often show certain similarities at different spatial scales.…”

Section: Handcrafted Fractal Featuresmentioning

confidence: 99%

“…The method proved relevant results for biological signal analysis [27,72,73]. The approach explored here considered the approach presented in [28], which expanded the analysis process by including a multidimensional and multiscale strategy.…”

Section: Multidimensional and Multiscale Higuchi Fractal Dimensionmentioning

confidence: 99%

“…In this process, as previously indicated, each pixel of the series under analysis was represented as a 5D vector (x, y, r, g, b) [28,41], allowing an analysis between the pixels involved in each transition of the sliding vector by the series. This procedure characterized the multidimensional approach, which is a useful strategy to quantify the patterns in color images.…”

Section: Multidimensional and Multiscale Higuchi Fractal Dimensionmentioning

confidence: 99%

“…The DF H value for a subseries was given by the angular coefficient obtained from the linear regression of a ln Len(d) × ln d plot, which was obtained through linear least squares fitting. The t auxiliary pixel subseries from each initial input set was employed to calculate the descriptor DF H [28]. Consequently, there are t values of DF H .…”

Section: Multidimensional and Multiscale Higuchi Fractal Dimensionmentioning

confidence: 99%

“…The first category consists of handcrafted features (HFs) defined by distinct extraction methods, usually aiming to overcome specific problems [18][19][20][21][22]. Among the HFs, it is possible to highlight techniques based on fractal geometry that use multiscale and multidimensional methods (Higuchi fractal dimension, probabilistic fractal dimension, box fusion fractal dimension, lacunarity and percolation) [23][24][25][26][27][28][29], Haralick [30] and local binary patterns (LBPs) [31]. For instance, Haralick and LBPs have been applied in several imaging contexts [32][33][34], exploring the identification of lung cancer subtypes [35], the presence of cancerous characteristics in breast tissue samples [18,36] and the classification of colorectal cancer [6].…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Tenguam,

Longo,

Roberto

et al. 2024

Applied Sciences

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In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.

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Section: Handcrafted Fractal Featuresmentioning

confidence: 99%

Section: Multidimensional and Multiscale Higuchi Fractal Dimensionmentioning

confidence: 99%

Section: Multidimensional and Multiscale Higuchi Fractal Dimensionmentioning

confidence: 99%

Section: Multidimensional and Multiscale Higuchi Fractal Dimensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Tenguam,

Longo,

Roberto

et al. 2024

Applied Sciences

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Classification of H&E images exploring ensemble learning with two-stage feature selection

Tenguam

Longo

Silva

et al. 2022

2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP)

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Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images

Cited by 2 publications

References 32 publications

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

Classification of H&E images exploring ensemble learning with two-stage feature selection

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