Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images

Martins, Alessandro Santana; Neves, Leandro Alves; Faria, Paulo Rogério de; Tosta, Tháına A. A.; Bruno, Daniel O. Tambasco; Longo, Leonardo C.; Nascimento, Marcelo Zanchetta do

doi:10.1007/978-3-030-33904-3_24

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…In [65], the fractal features extracted from RGB and L * a * b color spaces are used in classification of histopathology images of lymphoma. The fractal features are concatenated to be feature vectors and the Hermite polynomial classifier is selected to evaluate the performance of the method.…”

Section: Potential Methods Of Classification For Lhiamentioning

confidence: 99%

“…Besides, the number of applications has become more and more with the increase of time. The number of other tasks has also increased, such as quantitative analysis of images [64], analysis of characteristics of lymphoma [65] and so on.…”

Section: The Development Of Machine Vision In the Diagnosis And Treat...mentioning

confidence: 99%

“…In [49,65,89,[98][99][100][101][102][103][104][105][106][107][108][109][110][111][112], L * a * b * color space is used on LHIA for L * a * b * allowing color changes to be compatible with differences in visual perception. In [49,110,113,114], L * u * v * color space is used on LHIA for L * u * v * color space are uniform in perception.…”

Section: Color-based Preprocessing Techniquesmentioning

confidence: 99%

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What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Li¹,

Chen²,

Li³

et al. 2022

Preprint

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In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some more potential methods are proposed, and further prospects are made.

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Section: Potential Methods Of Classification For Lhiamentioning

confidence: 99%

Section: The Development Of Machine Vision In the Diagnosis And Treat...mentioning

confidence: 99%

Section: Color-based Preprocessing Techniquesmentioning

confidence: 99%

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What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Li¹,

Chen²,

Li³

et al. 2022

Preprint

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“…In addition, both lacunarity functions and percolation measures were also interpreted as scalar values in order to obtain representative descriptors of possible patterns existing in each observation [12,32,78,[80][81][82][83]. In these proposals, the authors were able to point out how some of these curves displayed a distinct behavior for each of the classes, making them relevant to the classification process.…”

Section: Handcrafted Features: Multiscale and Multidimensional Fracta...mentioning

confidence: 99%

Classification of Multiple H&E Images via an Ensemble Computational Scheme

Longo,

Roberto,

Tosta

et al. 2023

Entropy

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In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images.

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“…Fractal features have also provided relevant results on NHL classification recently. In [36], fractal geometry was used to extract multiscale and multidimensional features from RGB and LAB colored NHL images. The features extracted from the original images, without dataaugmentation, are given as input to a polynomial classifier.…”

Section: Non-hodgkin Lymphomas Classificationmentioning

confidence: 99%

Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

Roberto

Lumini

Neves

et al. 2021

Expert Systems with Applications

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Colour Feature Extraction and Polynomial Algorithm for Classification of Lymphoma Images

Cited by 10 publications

References 18 publications

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Classification of Multiple H&E Images via an Ensemble Computational Scheme

Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

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