Towards generalized nuclear segmentation in histological images

Vahadane, Abhishek; Sethi, Amit

doi:10.1109/bibe.2013.6701556

Cited by 26 publications

(30 citation statements)

References 6 publications

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“…Figs. 21 (e), 22 (e) and 23 (e) present the results after application of the method found in Vahadane and Sethi (2013) In comparison to manual segmentation ( Figs. 21 (b), 22 (b) and 23 (b)), the results provided by the proposed method ( Figs.…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 94%

“…Figs. 21 (e), 22 (e) and 23 (e) show the results obtained by Vahadane and Sethi (2013) technique on the different lymphoma classes. One observes that many regions were identified with several nuclei (marked by rectangular contours in red color), which result in a possible incorrect analysis of nuclear regions.…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

“…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%

“…In order to evaluate the proposed method and the aforementioned techniques, Tables 4-6 show their quantitative per-formance. These results were obtained by the metrics of accuracy, sensitivity, specificity and variation of information of the proposed method, mean-shift and techniques of Vahadane and Sethi (2013) , Wienert et al (2012) , de Oliveira et al (2013 , Phoulady et al (2016) and Paramanandam et al (2016) .…”

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

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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: 94%

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

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

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show abstract

Color-SIFT Features for Histopathological Image Analysis

Ouddai

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Ghézala

2023

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Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm

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et al. 2018

Applications of Evolutionary Computation

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For disease monitoring, grade definition and treatments orientation, specialists analyze tissue samples to identify structures of different types of cancer. However, manual analysis is a complex task due to its subjectivity. To help specialists in the identification of regions of interest, segmentation methods are used on histological images obtained by the digitization of tissue samples. Besides, features extracted from these specific regions allow for more objective diagnoses by using classification techniques. In this paper, fitness functions are analyzed for unsupervised segmentation and classification of chronic lymphocytic leukemia and follicular lymphoma images by the identification of their neoplastic cellular nuclei through the genetic algorithm. Qualitative and quantitative analyses allowed the definition of the Rényi entropy as the most adequate for this application. Images classification has reached results of 98.14% through accuracy metric by using this fitness function.

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Towards generalized nuclear segmentation in histological images

Cited by 26 publications

References 6 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

Color-SIFT Features for Histopathological Image Analysis

Fitness Functions Evaluation for Segmentation of Lymphoma Histological Images Using Genetic Algorithm

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