Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

Oliveira, Domingos Lucas Latorre de; Nascimento, Marcelo Zanchetta do; Neves, Leandro Alves; Godoy, Moacir Fernandes de; Arruda, Pedro Francisco Ferraz de; Neto, Dalisio de Santi

doi:10.1016/j.eswa.2013.06.079

Cited by 22 publications

(20 citation statements)

References 16 publications

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“…A common limitation of this method among the lesions was the identification of two nuclei as one object, indicated by red rectangles. ( Comaniciu & Meer, 2002 ) 79.74% 48% 86% 1.21 Vahadane and Sethi (2013) 77.07% 21% 88% 1.16 Wienert et al (2012) 78.53% 47% 84% 1.25 de Oliveira et al (2013) 70.60% 67% 71% 1.43 Phoulady et al (2016) 71.97% 70% 72% 3.77 Paramanandam et al (2016) 81.85% 4% 96% 0.42…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

“…The metrics of accuracy, sensitivity, specificity and variation of information were applied for quantitative evaluations. The performance of the proposed algorithm was compared to the results provided by the mean-shift technique ( Comaniciu & Meer, 2002 ) and the approaches proposed by de Oliveira et al (2013) ;Phoulady, Goldgof, Hall, and Mouton (2016) ; Vahadane and Sethi (2013) ; Wienert et al (2012) and Paramanandam et al (2016) .…”

Section: Introductionmentioning

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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Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…The problem of image segmentation has received a large attention in biomedical applications [1]- [3]. Indeed, the automatic segmentation of biomedical images is a critical step for quantifying the changes of anatomical structures that are highly related to biological tissue diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the automatic segmentation of biomedical images is a critical step for quantifying the changes of anatomical structures that are highly related to biological tissue diseases. For instance, global thresholding based on minimum cross entropy was adopted in [1] as a segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin, an atlas-aided fuzzy c-means (FCM-Atlas) was developed and validated in [2] to segment fibroglandular tissue and volumetric density estimation in breast MRI, and a sparse representation was adopted in [3] to fuse the multimodality image information and incorporate the anatomical constraints for brain tissue segmentation.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of particle swarm optimization techniques in segmentation of biomedical images

Lahmiri

Boukadoum

2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Image segmentation is a common image processing step to many computer vision applications with the purpose to segment pixels into different classes. As improved variants of particle swarm optimization (PSO) algorithms, the fractional-order Darwinian particle swarm optimization (FODPSO) and Darwinian particle swarm optimization (DPSO) have been proposed for image segmentation. The purpose of this paper is to compare the segmentation performance of PSO, DPSO, and FODPSO as parametric approaches to existing methods; namely the parametric fuzzy c-means (FCM) algorithm, and the non-parametric Otsu segmentation technique with application to five biomedical images. All PSO-based experiments are conducted with twenty runs to assess the effectiveness of PSO models. The universal quality index is used to evaluate the segmentation results. The obtained experimental results showed that particle swarm based algorithms outperformed both FCM and Otsu segmentation technique.

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“…Large amount of following studies have successively introduced the concepts of the expansion of Hartley entropy and Shannon entropy [16], relative entropy [17], cumulative residual entropy [18][19][20][21], joint entropy [22,23], conditional entropy [24][25][26], mutual information [27][28][29][30][31][32], cross entropy [33][34][35][36][37][38], fuzzy entropy [15,39], maximum entropy principle [40,41] and minimum cross-entropy principle [42,43], and a series of achievements have been made in these aspects. Zhong makes use of general information functions to unify the methods of describing information metrics with Entropy formulas [4].…”

Section: About the Metrics Of Informationmentioning

confidence: 99%

Objective information theory: A Sextuple model and 9 kinds of metrics

Shen

et al. 2014

2014 Science and Information Conference

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In the contemporary era, the importance of information is undisputed, but there has never been a common understanding of information, nor a unanimous conclusion to the researches on information metrics. Based on the previous studies, this paper analyzes the important achievements in the researches of the properties and metrics of information as well as their main insufficiencies, and discusses the essence and connotation, the mathematical expressions and other basic problems related to information. On the basis of the understanding of the objectivity of information, it proposes the definitions and a Sextuple model of objective information; discusses the basic properties of information, and brings forward the definitions and mathematical expressions of nine kinds of metrics of information, i.e., extensity, detailedness, sustainability, richness, containability, delay, distribution, validity and matchability. Through these, this paper establishes a basic theory frame of Objective Information Theory to support the analysis and research on information and information system systematically and comprehensively.

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Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

Cited by 22 publications

References 16 publications

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

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

An evaluation of particle swarm optimization techniques in segmentation of biomedical images

Objective information theory: A Sextuple model and 9 kinds of metrics

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