An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification

Tashk, Ashkan; Helfroush, Mohammad Sadegh; Danyali, Habibollah; Akbarzadeh, Mojgan

doi:10.1109/ikt.2013.6620101

Cited by 39 publications

(16 citation statements)

References 9 publications

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“…-Haralick texture features [10,7,11,14,13,15,2]; -Gabor filter [7,4,26]; -Local Binary Pattern (LBP) [22,25]; -First-order histogram statistics [4,15,16,20].…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Segmental Glomerulosclerosis in Kidney Histopathology

Araújo¹,

Schnitman²,

Duarte³

et al. 2018

Proceeding XIII Brazilian Congress on Computational Inteligence

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This paper presents the current results in the detection of segmental glomerulosclerosis by analyzing histological images of kidney biopsies, stained using hematoxylin and eosin (H&E) or periodic acid-Schiff (PAS) techniques. The work is part of the development of the PathoSpotter-K system, which aims the detection of elemental lesions in histological images of kidney. Currently, PathoSpotter-K accuracy for detecting segmental glomerulosclerosis is 84.8% for H&E stained samples and 81.3% for PAS stained samples. Such rates are similar to that reported for most of the analogous systems used for histological lesions detection in other organs and diseases.

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“…-Haralick texture features [10,7,11,14,13,15,2]; -Gabor filter [7,4,26]; -Local Binary Pattern (LBP) [22,25]; -First-order histogram statistics [4,15,16,20].…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Segmental Glomerulosclerosis in Kidney Histopathology

Araújo¹,

Schnitman²,

Duarte³

et al. 2018

Proceeding XIII Brazilian Congress on Computational Inteligence

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“…The second approach (IPAL) computed co-occurrence, run-length and scale-invariant feature transform features for mitosis and nonmitosis patches [18]. The third approach (SUTECK) used completed local binary patterns pixel-wise SVM classification in mitosis detection [65,14]. The NEC team [21] and CCIPD/MINDLAB team [19] employed the learned CNN-derived features for mitosis detection.…”

Section: Comparative Strategiesmentioning

confidence: 99%

Automated mitosis detection in histopathology based on non-gaussian modeling of complex wavelet coefficients

et al. 2017

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractTo diagnose breast cancer, the number of mitotic cells present in histology sections is an important indicator for examining and grading biopsy specimen. This study aims at improving the accuracy of automated mitosis detection by characterizing mitotic cells in wavelet based multi-resolution representations via a non-Gaussian modeling method. The potential mitosis candidates were decomposed into multi-scale forms by an undecimated dual-tree complex wavelet transform. Two non-Gaussian models (the generalized Gaussian distribution (GGD) and the symmetric alpha-stable (SαS) distributions) were used to accurately model the heavy-tailed behavior of wavelet marginal distributions. The method was evaluated on two independent data cohorts, including the benchmark dataset (MITOS), via a support vector machine classifier.

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“…In some researches, for mitosis detection purpose, artificial neural networks (ANNs) [14] and exclusive independent component analysis (EICA) [15] have been employed. In some other more recently proposed papers such as [18][19][20], specific features with object-wise extraction considerations are proposed. This approach leads to better discrimination results between mitotic and nonmitotic objects.…”

Section: Computational Biology Journalmentioning

confidence: 99%

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

Nateghi

Danyali

Helfroush

et al. 2014

Computational Biology Journal

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This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

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An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification

Cited by 39 publications

References 9 publications

Automated Detection of Segmental Glomerulosclerosis in Kidney Histopathology

Automated Detection of Segmental Glomerulosclerosis in Kidney Histopathology

Automated mitosis detection in histopathology based on non-gaussian modeling of complex wavelet coefficients

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

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