A study about color normalization methods for histopathology images

Roy, Santanu; Jain, Alok; Lal, Shyam; Kini, Jyoti R.

doi:10.1016/j.micron.2018.07.005

Cited by 123 publications

(56 citation statements)

References 20 publications

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“…Hematoxylin and Eosin (H&E) are probably the most widely used combination of stains. Since Color Deconvolution (CD), that is, H&E separation, is a very important preprocessing step, several methods have been developed (see [5] for a recent review). One of the first CD methods, which is widely used, was proposed by Ruifrok et al [6].…”

Section: Introductionmentioning

confidence: 99%

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

Esteban

López-Pérez

Colomer

et al. 2019

Computer Methods and Programs in Biomedicine

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Background and objective: Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. Method: We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become * Corresponding author. The first two authors contributed equally.

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Section: Introductionmentioning

confidence: 99%

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

Esteban

López-Pérez

Colomer

et al. 2019

Computer Methods and Programs in Biomedicine

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“…Unlike CT scans or ultrasound images, which are grayscale images, WSIs of tissues are color images and more likely to suffer from color variations due to different staining conditions. Staining inconsistencies may be attributed to multiple factors: dye, staining protocols of laboratories, fading, and digital scanners ( Vahadane et al, 2016 ; Roy et al, 2018 ). To ease the adverse impact upon the analytic accuracy of AI, multiple scholars have tried various methods to standardize color distribution in images ( Khan et al, 2014 ; Vicory et al, 2015 ; Bejnordi et al, 2016 ; Samsi et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Guo

Hu³

et al. 2020

Front. Pharmacol.

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Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.

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“…Histopathological images take up stains inconsistently which results in colour variations in the images [23]. The presence of artifacts and errors in microscope during image acquisition results in illumination variations in histopathological images [23]. Handling these variations is difficult which leads to inappropriate results [23].…”

Section: Related Methodsmentioning

confidence: 99%

A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images

et al. 2020

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Breast histopathological image analysis helps in understanding the structure and distribution of the nucleus, thereby assisting in the detection of breast cancer. But analysis of histopathological image is challenging due to various reasons such as heterogeneity of nucleus structure, overlapping nuclei, clustered nuclei, variations in illumination, presence of noise etc. Limited availability of breast histopathological image dataset with fine annotations for detection of nucleus has restricted the analysis of histopathological images at the pixel-level. This paper presents fine annotations for nucleus segmentation of breast histopathological image datasets. Various textures such as Filter Banks, Gray Level Co-occurrence matrix and Local Binary Patterns are studied along with colour features for semantic segmentation of nuclei from histopathological images. Support Vector Machine and Multi Layer Perceptron algorithms are trained to perform pixelwise classification. The performance of the three texture features are evaluated on the two datasets and the results are presented in this paper. INDEX TERMS Computer-aided diagnostic, semantic segmentation, texture features, machine learning.

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A study about color normalization methods for histopathology images

Cited by 123 publications

References 20 publications

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images

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