Segmentation of leukocyte cells in bone marrow smears

Zerfas, Thorsten; Haslmeyer, Erik; Schlarb, Timo; Elter, Matthias

doi:10.1109/cbms.2010.6042654

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…However, the images used for experimentation are nonstandard. Zerfass, Haslmeyer, Schlarb, and Elter (2010), actual slides of bone marrow are tested having WBCs. Otsu thresholding algorithm is applied two times, first for RBCs and platelets elimination, the second time for plasma background elimination.…”

Section: Inductive Segmentation Approachesmentioning

confidence: 99%

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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Malaria parasitemia diagnosis and grading is hard and still far from perfection. Inaccurate diagnosis and grading has caused tremendous deaths rate particularly in young children worldwide. The current research deeply reviews automated malaria parasitemia diagnosis and grading in thin blood smear digital images through image analysis and computer vision based techniques. Actually, state-of-the-art reveals that current proposed practices present partially or morphology dependent solutions to the problem of computer vision based microscopy diagnosis of malaria parasitemia. Accordingly, a deep appraisal of the current practices is investigated, compared and analyzed on benchmark datasets. The open gaps are highlighted and the future directions are laid down for a complete automated microscopy diagnosis for malaria parasitemia based on those factors that have not been affected by other diseases. Moreover, a general computer vision framework to perform malaria parasitemia estimation/grading is constructed in universal directions. Finally, remaining problems are highlighted and possible directions are suggested. RESEARCH HIGHLIGHTS: The current research presents a microscopic malaria parasitemia diagnosis and grading of malaria in thin blood smear digital images through image analysis and computer vision based techniques. The open gaps are highlighted and future directions for a complete automated microscopy diagnosis of malaria parasitemia mentioned.

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Section: Inductive Segmentation Approachesmentioning

confidence: 99%

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

Rehman

Abbas

Saba

et al. 2018

Microscopy Res & Technique

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“…Finally, a new SVM was trained by a training set sampling from cytoplasm and three modes to extract the whole leukocytes. Reference [6] presents a new algorithm which was robust concerning variations as color fluctuations in the bone marrow images. The evaluation of this algorithm was done by comparing the segmentation results with the results obtained by an existing algorithm.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Marrow Cells Images Based on Fuzzy C-Means Clustering

Guo

Wang

2012

AMM

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When a person watches different marrow-cell images he or she can identify every type of cells easily. In this process, human’s visual system has ability to adapt the different shades of the color marrow cells images. We propose a segmentation method for marrow-cell images based on fuzzy c-means clustering (FCM). Firstly, the count of cluster is calculated out using the shades of the R-matrix of a RGB formatted marrow cells image. Secondly, the fuzzy c-means clustering method is done on the R-matrix. Finally, the pixel of G-matrix and B-matrix are divided into some clusters by “one to one correspondence” of the position of pixels that belong to R-matrix, G-matrix or B-matrix. This paper’s contribution could be summarized into three points: 1) a frame work of the fuzzy c-means clustering for marrow-cell images segmentation is proposed. 2) Using FCM and the R- matrix component of a RGB formatted marrow-cell images to generate the count of clustering. 3) This method could adaption different shades of different marrow-cell images.

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