A Hermite polynomial algorithm for detection of lesions in lymphoma images

Martins, Alessandro Santana; Neves, Leandro Alves; Faria, Paulo Rogério de; Tosta, Tháına A. A.; Longo, Leonardo C.; Silva, Adriano B.; Roberto, Guilherme Freire; Nascimento, Marcelo Zanchetta do

doi:10.1007/s10044-020-00927-z

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…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%

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: Color-based Preprocessing Techniquesmentioning

confidence: 99%

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

Li¹,

Chen²,

Li³

et al. 2022

Preprint

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“…All works presented in the table use more complex descriptors demanding more expensive image processing and hinder their model's interpretation by doctors, who are not familiar with this type of data. Furthermore, their methods are generally black-box, or at least, not trivial to be understood by non-specialists, such as SVM [6], [16], [25], neural networks [20] and polynomial classifiers [13], where the information is embedded in the model or requires an expensive training. In the other hand, our methodology uses simpler and easier to extract descriptors, and employs the GBDT algorithm, providing…”

Section: Correlated Workmentioning

confidence: 99%

“…Unfortunately, this crucial task is not trivial, even to specialists. The analysis of histological images can be used in this process, but inter and intra-pathologist variability in the diagnosis can occur, caused by the subjectivity of human analysis [13]. Computer aided diagnosis (CAD) systems are tools designed to facilitate physicians in the analysis of medical exams.…”

Section: Introductionmentioning

confidence: 99%

“…The extraction of descriptors from medical images and their statistical analysis can lead to new features and aid in the discoveries regarding the subject diseases [5]. However, many works either do not segment the image to find information in the cell nuclei or they use complex descriptors, that can not be easily interpreted by a specialist [6], [13], [14]. In [17], the authors extract simple data from the cell nuclei related to their color and shape, but they only perform binary classification.…”

Section: Introductionmentioning

confidence: 99%

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A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors

Faria

Nascimento

Martins

2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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Lymphoma is one of the most common types of cancer and its treatment can be more effective if the disease variant is correctly diagnosed. Many works have been done using computer vision and machine learning to classify the images. This work presents lymphoma based on histological a method using simple descriptors and a decision tree-based ensemble classifier, aiming to maintaing the interpretability of the data and understand what information in most important to the classification task. We use morphological and non morphological descriptors extracted from the cells nuclei, a feature selection method based on principal component analysis (PCA), and a gradient boosting decision tree (GBDT) method for multiclass classification. Our approach achieves an average accuracy of 0.932. this result is close to those obtained in the state of the art, while it uses simpler descriptors and better interpretable classification models.

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An ensemble of learned features and reshaping of fractal geometry-based descriptors for classification of histological images

Roberto,

Neves,

Lumini

et al. 2024

Pattern Anal Applic

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A Hermite polynomial algorithm for detection of lesions in lymphoma images

Cited by 10 publications

References 37 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

A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors

An ensemble of learned features and reshaping of fractal geometry-based descriptors for classification of histological images

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