An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation

Settouti, Nesma; Saidi, Meryem; Bechar, Mohammed El Amine; Daho, Mostafa El Habib; Chikh, Mohammed Amine

doi:10.1007/s10044-020-00873-w

Cited by 6 publications

(3 citation statements)

References 53 publications

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“…21 This method has shown great promise, in comparison with others, in the classification of histological image data in recent papers, including blood smears. 14,[22][23][24] After classification, the resulting binary image was submitted to a morphological opening (an erosion followed by dilation) by a (5, 5) kernel of ellipsoidal format, followed by a connected components analysis (CCA) with statistics, both functions available on OpenCV library. The opening kernel was an ellipsoidal one, with a size of (5,5), and the parameters of connectivity and ltype, for CCA, as being to 8 and CV 32S, respectively.…”

Section: Image Segmentationmentioning

confidence: 99%

White blood cells segmentation and classification using a random forest and residual networks implementation

Rodrigues Garcia,

Ponce Ayala,

Pratavieira

et al. 2024

Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences

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Artificial intelligence algorithms are interesting solutions to automate the tedious manual counting of white blood cells by a specialist. Although interesting machine learning algorithms have been proposed for this task, there is a lack in the literature for high-accuracy methods (more than 99%) tested on larger datasets (more than 10 thousand images). This paper presents a segmentation and classification methodology, based on Random Forest and ResNet50, along with a comparison between ResNet models with different numbers of layers. The segmentation was tested in microscope-like images mounted using multiple single-cell images, widely available in online datasets, yielding 300×300 images to be classified by the residual network. For image classification, ResNet50 reached higher accuracies (99.3%, to the best of our knowledge, the higher accuracy for models with more than 1000 images), with the model size comparison pointing to model overfitting for larger models.

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Section: Image Segmentationmentioning

confidence: 99%

White blood cells segmentation and classification using a random forest and residual networks implementation

Rodrigues Garcia,

Ponce Ayala,

Pratavieira

et al. 2024

Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences

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“…For the automatic segmentation of WBC , 18 introduced a random forest‐RI ensemble methods‐based approach for instance and variable selection to filter out noisy and unnecessary information 19 . proposed an improvement of the mask RCNN by adding and adjusting hyper‐parameters and spatial information.…”

Section: Related Workmentioning

confidence: 99%

Modified U‐Net for cytological medical image segmentation

Benazzouz

Benomar

Moualek

2022

Int J Imaging Syst Tech

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Deep learning–based medical image segmentation is henceforth widely established as a powerful segmentation process. This article proposes a new U‐Net architecture based on a convolutional neural network for cytology image segmentation. This structure is more suitable to take into account pixel neighborhood in deconvolution. The goal is to develop an accurate segmentation method for white blood cells segmentation based on cells types features. This new proposed method yields a significant improvement compared to our previous work on the cytological medical dataset. In addition, the performance of the new architecture was also successfully tested on the Digital Retinal Image for Vessel Extraction databases benchmark. The images of this challenge are similar to our cytology image segmentation. Our approach achieved 25% relative improvement of the accuracy compared to the state‐of‐the‐art.

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“…There are various types of segmentation techniques commonly used by researchers such as thresholding, edge-based, region-based, watershed, clustering-based, and neural network. Some recent research, builds the detection methods from a combination of known segmentation techniques rather than proposing detection techniques with new approaches (Abdurrazzaq et al, 2021;Liang et al, 2018;Settouti et al, 2020;Yao et al, 2021;Z. et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

White blood cell segmentation by using vector color approach

Abdurrazzaq¹,

Junoh²,

Yulianto³

2023

App.Sci.Def

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White blood cells play an important role in protecting the body from various types of diseases, therefore blood tests are done to determine a person’s disease. Diagnosis of blood diseases is done by analyzing and counting the white blood cells in the blood. Optimal calculations and analysis of white blood cell are important, so research on white blood cell detection is a rapidly growing topic. Many detection methods are proposed using the existing segmentation techniques. In this paper, the detection method is proposed using vector operation approach instead general segmentation technique. From the experiments performed, it is shown that the proposed method can detect white blood cells on smear images. Further, this method extracts the nucleus from white blood cells well.

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An instance and variable selection approach in pixel-based classification for automatic white blood cells segmentation

Cited by 6 publications

References 53 publications

White blood cells segmentation and classification using a random forest and residual networks implementation

White blood cells segmentation and classification using a random forest and residual networks implementation

Modified U‐Net for cytological medical image segmentation

White blood cell segmentation by using vector color approach

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