Semiautomatic White Blood Cell Segmentation Based on Multiscale Analysis

Dorini, Leyza Baldo; Minetto, Rodrigo; Leite, Neucimar J.

doi:10.1109/titb.2012.2207398

Cited by 89 publications

(29 citation statements)

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“…In [199], a self-dual multiscale morphological toggle operator is derived from scale-space gray-scale morphology [200], and it is adopted to improve gradient images with edge enhancing, thereby yielding better cell segmentation accuracy in white blood cell images. The top-hat transform used to reduce the effects of color diffusion for cell segmentation is reported in [41] and gray-scale reconstruction is applied to cell clump splitting in [58].…”

Section: Nucleus and Cell Segmentation Methodsmentioning

confidence: 99%

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing

Yang

2016

IEEE Rev. Biomed. Eng.

442

220

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Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to inter-observer variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literatures. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast (DIC), fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

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Section: Nucleus and Cell Segmentation Methodsmentioning

confidence: 99%

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing

Yang

2016

IEEE Rev. Biomed. Eng.

442

220

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“…To lessen the computational burden, Fishers linear discriminant was also applied to trim a multi-dimensional set to six dimensions. In more recent work (2012) Dorini et al [9] introduced automatic differential cell system in two levels to segment WBC nucleus and identify the cytoplasm region. In that work, five mature WBC types were classified using a K-Nearest Neighbor (K-NN) classifier with geometrical shape features and a reasonable accuracy (78% performance vs 85% classified manually by a specialist) was achieved.…”

Section: Background and Literature Surveymentioning

confidence: 99%

Comparative Study of Feature Selection for White Blood Cell Differential Counts in Low Resolution Images

Habibzadeh

Krzyżak

Fevens

2014

Advanced Information Systems Engineering

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Abstract.Features that are widely used in digital image analysis and pattern recognition tasks are from three main categories: shape, intensity, and texture invariant features. For computer-aided diagnosis in medical imaging for many specific types of medical problem, the most effective choice of a subset of these features through feature selection is still an open problem. In this work, we consider the problem of white blood cell (leukocyte) recognition into their five primary types: Neutrophils, Lymphocytes, Eosinophils, Monocytes and Basophils using a Support Vector Machine classifier. For features, we use four main intensity histogram calculations, set of 11 invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform, Haralick and Tamura features. Global sensitivity analysis using Sobol's RS-HDMR which can deal with independent and dependent input variables is used to assess dominate discriminatory power and the reliability of feature models in presence of high dimensional input feature data to build an efficient feature selection. Both the numerical and empirical results of experiments are compared with forward sequential feature selection. Finally, the results obtained from the preliminary analysis of white blood cell classification are presented in confusion matrices and interpreted using Cohen's kappa (κ) with the classification framework being validated with experiments conducted on poor quality white blood cell images.

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“…The traditional method of classifying white blood cells relied on observation of a blood smear through microscopy, where the identification process is based on visible features such as color and shape. However, medical operators' knowledge and experience play a decisive role in the correctness of WBCs analysis, making the process time-consuming and variable [1][2][3]. To address these limitations, computer-aided identification methods have been developed to replace the manual method.…”

Section: Introductionmentioning

confidence: 99%