Hybrid framework based on evidence theory for blood cell image segmentation

Baghli, Ismahan; Nakib, Amir; Sellam, Elie; Benazzouz, Mourtada; Chikh, Amine; Petit, Éric

doi:10.1117/12.2042142

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…These results are significantly better compared to our previous work in terms of segmentation quality and in terms of time and computing [7], [8].…”

Section: ) Shape Prior For Cytological Image Segmentationmentioning

confidence: 55%

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Automated identification of plasma cell in bone marrow images

Benazzouz

Baghli

Benomar

et al. 2017

2017 International Conference on Mathematics and Information Technology (ICMIT)

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The classification and the count of different White Blood Cells (WBC) in microscopy images allow the assessment of a wide range of important hematology pathologies such Myeloma. This article aims particularly myeloma disease; this pathology is manifested by a proliferation of a type of cell called plasma cells. This paper presents a robust and accurate novel method for processing WBC(leukocyte) using a combination of ideas. The segmentation of cells is achieved in two phases, the first is to extract the nucleus from the green channel of the image using the otsu's thresholding method, and the second operates through a region growing on the nucleus in order to delimitate a cytoplasm. After that, a cell's characterization step is performed where desired features can be extracted in order to classify types of leukocytes and thus allow the counting of plasma cells. The proposed process have been successfully tested to a large number of images, showing promising results in terms of segmentation and classification of plasma cells, and also applied to varying cell appearance and image quality, encouraging future works.

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“…These results are significantly better compared to our previous work in terms of segmentation quality and in terms of time and computing [7], [8].…”

Section: ) Shape Prior For Cytological Image Segmentationmentioning

confidence: 55%

“…classifying separately the data from different sources(color spaces) then merging decisions, or combining these data to classify them [7]. The second [8] deals with new segmentation framework based on evidence theory, called ESA (Evidential Segmentation Algorithm).…”

Section: Introductionmentioning

confidence: 99%

Automated identification of plasma cell in bone marrow images

Benazzouz

Baghli

Benomar

et al. 2017

2017 International Conference on Mathematics and Information Technology (ICMIT)

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“…Features extraction and cells classification is presented in Benazzouz et al (2015). Segmentation scheme using pixel classification based on the fusion of information and evidential algorithm to segment blood cell images is reported in Benazzouz et al (2013Benazzouz et al ( , 2016 and Baghli et al (2014).…”

Section: Related Workmentioning

confidence: 99%

Multi-feature-based approach for white blood cells segmentation and classification in peripheral blood and bone marrow images

Benomar

Chikh

Descombes

et al. 2021

IJBET

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In this paper, we propose a complete automated framework for white blood cells differential count in peripheral blood and bone marrow images, in order to reduce the analysis time and increase the accuracy of several blood disorders diagnosis. A new colour transformation is first proposed to highlight the white blood cells regions; then, a marker controlled watershed algorithm is used to segment the region of interest. The nucleus and cytoplasm are subsequently separated. In the identification step, a set of colour, texture and morphological features are extracted from both nucleus and cytoplasm regions. Next, the performances of a random forest classifier on a set of microscopic images are compared and evaluated. The obtained results reveal high recognition accuracies for both segmentation and classification stage.

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“…Image analysis plays an important role in several research fields such as robotics and manufacturing [1], [2], biology [3], medicine [4] and remote sensing [5], since meaningful information can be extracted from this in order to be used in tasks as recognition of bar coded and human face, for instance [6]. Otherwise, image segmentation is a critical and essential part of any image analysis system.…”

Section: Introductionmentioning

confidence: 99%

Thresholding of biological images by using evolutionary algorithms

Ochoa-Montiel

Sánchez–López

Gonzalez

2015

2015 Latin America Congress on Computational Intelligence (LA-CCI)

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This paper addresses the thresholding of biological images through multiobjective optimization techniques. Three objective functions are used during the optimization, which are combined at pairs: Shannon entropy versus Otsu's inter-class and Shannon entropy versus Otsu's intra-class. We show that although both combinations are obtaining the same vector of thresholds, the first objective function pair presents less computational effort to compute the Pareto front. Furthermore, we have also show that the size of the initial population of the evolutionary algorithm can be selected as 1/10 of the full space. As a consequence, Pareto fronts can quickly be computed and without affecting its performance and diversity.

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Hybrid framework based on evidence theory for blood cell image segmentation

Cited by 7 publications

References 12 publications

Automated identification of plasma cell in bone marrow images

Automated identification of plasma cell in bone marrow images

Multi-feature-based approach for white blood cells segmentation and classification in peripheral blood and bone marrow images

Thresholding of biological images by using evolutionary algorithms

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