Application of Support Vector Machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation

Mohammed, Emad A.; Far, Behrouz H.; Naugler, Christopher; Mohamed, Mostafa M.

doi:10.1109/healthcom.2013.6720751

Cited by 20 publications

(24 citation statements)

References 6 publications

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“…The main steps in image analysis are the following: (i) image acquisition and preprocessing, (ii) segmentation, and (iii) extraction of features (quantitative descriptors), which make possible the further classification of the different cell types.…”

Section: Quantitative Morphological Features Based On Image Analysismentioning

confidence: 99%

Optimizing morphology through blood cell image analysis

Merino

Puigví

Boldú

et al. 2018

Int J Lab Hematology

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Section: Quantitative Morphological Features Based On Image Analysismentioning

confidence: 99%

Optimizing morphology through blood cell image analysis

Merino

Puigví

Boldú

et al. 2018

Int J Lab Hematology

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show abstract

“…In Mohammed, Far, Naugler, and Mohamed (2013b) , the authors employed Otsu thresholding, canny edge detector, morphological operations and removal of 1% of local minima of watershed to reduce over and under segmentation errors. Further, the authors presented in Mohammed et al (2013a) , a segmentation method based on pixel classification using support vector machine (SVM) and K-means to reduce the feature set.…”

Section: Related Workmentioning

confidence: 99%

“…The works of Belkacem-Boussaid et al (2011) and Kong, Gurcan, and Belkacem-Boussaid (2011b) considered, respectively, only three and five images for defining the parameters of the algorithm, which can be inadequate for application in image datasets. In the approaches of Mohammed et al (2013aMohammed et al ( , 2013b, only images with ROIs in their central regions were investigated. Besides, Mohammed et al (2013a) assumed in their proposed approach uniform illumination of the images for Otsu application.…”

Section: Related Workmentioning

confidence: 99%

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Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm

Tosta

Faria

Neves

et al. 2017

Expert Systems with Applications

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a b s t r a c tNon-Hodgkin lymphoma is the most common cancer of the lymphatic system and should be considered as a group of several closely related cancers, which can show differences in their growth patterns, their impact on the body and how they are treated. The diagnosis of the different types of neoplasia is made by a specialist through the analysis of histological images. However, these analyses are complex and the same case can lead to different understandings among pathologists, due to the exhaustive analysis of decisions, the time required and the presence of complex histological features. In this context, computational algorithms can be applied as tools to aid specialists through the application of segmentation methods to identify regions of interest that are essential for lymphomas diagnosis. In this paper, an unsupervised method for segmentation of nuclear components of neoplastic cells is proposed to analyze histological images of lymphoma stained with hematoxylin-eosin. The proposed method is based on the association among histogram equalization, Gaussian filter, fuzzy 3-partition entropy, genetic algorithm, morphological techniques and the valley-emphasis method in order to analyze neoplastic nuclear components, improve the contrast and illumination conditions, remove noise, split overlapping cells and refine contours. The results were evaluated through comparisons with those provided by a specialist and techniques available in the literature considering the metrics of accuracy, sensitivity, specificity and variation of information. The mean value of accuracy for the proposed method was 81.48%. Although the method obtained sensitivity rates between 41% and 51%, the accuracy values showed relevance when compared to those provided by other studies. Therefore, the novelties presented here may already encourage new studies with a more comprehensive overview of lymphoma segmentation.

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“…4 For segmentation, methods such as HSV segmentation, 5,6 image enhancement segmentation, 7 gradient vector°o w snake segmentation 8 and supported vector machine (SVM) segmentation 9 are standard in literature. In general, well extracted features can compensate the e®ectiveness of segmentation for better recognition rate.…”

Section: Introductionmentioning

confidence: 99%

A robust automatic leukocyte recognition method based on island-clustering texture

Cao

2016

J. Innov. Opt. Health Sci.

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A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT) is proposed. By analyzing the features of the¯ve typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the¯ve typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.

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Application of Support Vector Machine and k-means clustering algorithms for robust chronic lymphocytic leukemia color cell segmentation

Cited by 20 publications

References 6 publications

Optimizing morphology through blood cell image analysis

Optimizing morphology through blood cell image analysis

Computational method for unsupervised segmentation of lymphoma histological images based on fuzzy 3-partition entropy and genetic algorithm

A robust automatic leukocyte recognition method based on island-clustering texture

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