Unsupervised colour segmentation of white blood cell for acute leukaemia images

Nasir, Aimi Salihah Abdul; Mashor, Mohd Yusoff; Hassan, Rosline

doi:10.1109/ist.2011.5962188

Cited by 22 publications

(4 citation statements)

References 2 publications

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“…Nasir et. al 2011 [19] provided a method based on the application of K Means Clustering on the hue channel image which solved the problem of segmenting the nucleus only from the images.…”

Section: Related Workmentioning

confidence: 99%

Eminent Teachers of the Department of Psychology at Calcutta University

Bhattacharya

2021

Psychology in Modern India

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It is known that static traversable wormhole in Einstein gravity is supported by matter that violates null energy conditions (NEC). Essentially such wormhole will be characterised by a central throat with anisotropic matter lining the throat that violates NEC. This in turn provides viable geometry for the wormhole to sustain. In 2018, L. Herrera introduced a new classification for spherically symmetric bodies called "complexity factor". It was proposed that a spherically symmetric non trivial geometry can be classified as complex or non-complex based on the nature of the inhomogeneity and anisotropy of the stress energy tensors with only homogeneous and isotropic matter distribution leading to null complexity. Mathematically there was also another way of obtaining zero complexity geometry. In this context since static traversable wormhole by default is characterised by anisotropic and inhomogeneous matter stress tensors, the question we answer is whether it is possible to obtain zero complexity class of wormholes supported by exotic matter.

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“…Nasir et. al 2011 [19] provided a method based on the application of K Means Clustering on the hue channel image which solved the problem of segmenting the nucleus only from the images.…”

Section: Related Workmentioning

confidence: 99%

Eminent Teachers of the Department of Psychology at Calcutta University

Bhattacharya

2021

Psychology in Modern India

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“…Contrast enhancement (using intensity stretching techniques) and segmentation (based on hue, saturation, intensity model) improve the visibility and decompose the leukemia images into blast and nucleus parts [4]. Unsupervised k-means clustering along with contrast enhancement and the color transformation has been used for segmentation of cytoplasm and nucleus regions [5]. WBC detection has been considered as circle fitting problem in [6] and electromagnetism-like optimization algorithm has been used.…”

Section: Introductionmentioning

confidence: 99%

“…In [11], an imaging system based upon acousto-optic tunable filter has been proposed to detect the WBCs. However, the existing methods [2][3][4][5][6][7][8], [11] have limited accuracy in the detection of overlapping cells. In [12], a circle detection algorithm has been used to count red blood cells (RBCs) and WBCs.…”

Section: Introductionmentioning

confidence: 99%

Overlapping White Blood Cells Detection Based on Watershed Transform and Circle Fitting

Sukhia¹,

Riaz²,

Ghafoor³

et al. 2017

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“…Abdul Nasir et al . () used K‐means clustering approach on hue and saturation colour space for WBC segmentation. A modified clustering approach with the stimulating discriminating measure proposed for WBC segmentation (Neoh et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Automated identification of normoblast cell from human peripheral blood smear images

Das

Maiti

Chakraborty

2017

Journal of Microscopy

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In this paper, we have presented a new computer-aided technique for automatic detection of nucleated red blood cells (NRBCs) or normoblast cell from peripheral blood smear image. The proposed methodology initiates with the localization of the nucleated cells by adopting multilevel thresholding approach in smear images. A novel colour space transformation technique has been introduced to differentiate nucleated blood cells [white blood cells (WBCs) and NRBC] from red blood cells (RBCs) by enhancing the contrast between them. Subsequently, special fuzzy c-means (SFCM) clustering algorithm is applied on enhanced image to segment out the nucleated cell. Finally, nucleated RBC and WBC are discriminated by the random forest tree classifier based on first-order statistical-based features. Experimentally, we observed that the proposed technique achieved 99.42% accuracy in automatic detection of NRBC from blood smear images. Further, the technique could be used to assist the clinicians to diagnose a different anaemic condition.

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Unsupervised colour segmentation of white blood cell for acute leukaemia images

Cited by 22 publications

References 2 publications

Eminent Teachers of the Department of Psychology at Calcutta University

Eminent Teachers of the Department of Psychology at Calcutta University

Overlapping White Blood Cells Detection Based on Watershed Transform and Circle Fitting

Automated identification of normoblast cell from human peripheral blood smear images

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