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Made available in DSpace on 2020-12-10T19:39:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-10-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential limitation, normalization methods are useful for color correction. In this paper, a histological image color normalization is presented by considering the biological and hematoxylin-eosin properties. To this end, the stain representation of a reference image was applied in place of the original images representation, allowing the preservation of histological structures. This proposal was evaluated on histological images with great variations of contrast, and both visual and quantitative analyzes yielded promising results. (C) 2019 Elsevier Ltd. All rights reserved. Fed Univ ABC, Ctr Math Comp & Cognit, Av Estados 5001, BR-09210580 Santo Andre, SP, Brazil Univ Fed Uberlandia, Inst Biomed Sci, Dept Histol & Morphol, Av Amazonas S-N, BR-38405320 Uberlandia, MG, Brazil Univ Uberaba, Sch Dent, Av Nene Sabino 1801, BR-38055500 Uberaba, MG, Brazil Sao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil Univ Fed Uberlandia, Fac Comp Sci, Av Joao Naves de Avila 2121, BR-38400902 Uberlandia, MG, Brazil Fed Inst Triangulo Mineiro, R Belarmino Vilela Junqueira S-N, BR-38305200 Ituiutaba, MG, Brazil Sao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil CAPES: 001 CNPq: 427114/2016-0 CNPq: 304848/2018-2 CNPq: 430965/2018-4 CNPq: 313365/2018-0 FAPEMIG: APQ-00578-18 CAPES: 1575210

Made available in DSpace on 2020-12-10T19:36:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-08-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Histological samples stained with hematoxylin-eosin (H&E) are commonly used by pathologists in cancer diagnoses. However, the preparation, digitization, and storage of tissue samples can lead to color variations that produce poor performance when using histological image processing techniques. Thus, normalization methods have been proposed to adjust the color of the image. This can be achieved through the use of a spectral matching technique, where it is first necessary to estimate the H&E representation and the stain concentration in the image pixels by means of the RGB model. This study presents an estimation method for H&E stain representation for the normalization of faded histological samples. This application has been explored only to a limited extent in the literature, but has the capacity to expand the use of faded samples. To achieve this, the normalized images must have a coherent color representation of the H&E stain with no introduction of noise, which was realized by applying the methodology described in this proposal. The estimation method presented here aims to normalize histological samples with different degrees of fading using a combination of fuzzy theory and the Cuckoo search algorithm, and dictionary learning with an initialization method for optimization. In visual and quantitative comparisons of estimates of H&E stain representation from the literature, our proposed method achieved very good results, with a high feature similarity between the original and normalized images. Fed Univ ABC, Ctr Math Comp & Cognit, Av Estados 5001, BR-09210580 Santo Andre, SP, Brazil Univ Fed Uberlandia, Inst Biomed Sci, Dept Histol & Morphol, Av Amazonas S-N, BR-38405320 Uberlandia, MG, Brazil Sao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil Univ Fed Uberlandia, Fac Comp Sci, Av Jodo Naves de Avila 2121, BR-38400902 Uberlandia, MG, Brazil Sao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP, Brazil CAPES: 001 CNPq: 304848/2018-2 CNPq: 430965/2018-4 CNPq: 313365/2018-0 FAPEMIG: APQ-00578-18 CAPES: 1575210

Made available in DSpace on 2018-11-26T17:31:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-09-15 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Non-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. (C) 2017 Elsevier Ltd. All rights reserved. Fed Univ ABC, Ctr Math Comp & Cognit, Ave Estados 5001, BR-09210580 Sao Paulo, Brazil Univ Fed Uberlandia, Inst Biomed Sci, Dept Histol & Morphol, Ave Amazonas S-N, BR-38405320 Uberlandia, MG, Brazil Sao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, BR-15054000 Sao Paulo, Brazil Sao Paulo State Univ, Dept Comp Sci & Stat, R Cristovao Colombo 2265, BR-15054000 Sao Paulo, Brazil CAPES: 1575210 FAPEMIG: TEC - APQ-02885-15