A general framework for the segmentation of follicular lymphoma virtual slides

Oger, Myriam; Belhomme, Philippe; Gürcan, Metin N.

doi:10.1016/j.compmedimag.2012.05.003

Cited by 34 publications

(30 citation statements)

References 18 publications

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“…It is rapidly fatal if not immediately treated with aggressive chemotherapy [21]. Therefore, accurate grading of follicular lymphoma images is of course essential to the optimal choice of treatment.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale directional-filtering-based method for follicular lymphoma grading

Bozkurt

Suhre

Çetin

2014

SIViP

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Follicular lymphoma (FL) is a group of malignancies of lymphocyte origin that arise from lymph nodes, spleen, and bone marrow in the lymphatic system. It is the second most common non-Hodgkins lymphoma. Characteristic of FL is the presence of follicle center B cells consisting of centrocytes and centroblasts. Typically, FL images are graded by an expert manually counting the centroblasts in an image. This is time consuming. In this paper, we present a novel multi-scale directional filtering scheme and utilize it to classify FL images into different grades. Instead of counting the centroblasts individually, we classify the texture formed by centroblasts. We apply our multi-scale directional filtering scheme in two scales and along eight orientations, and use the mean and the standard deviation of each filter output as feature parameters. For classification, we use support vector machines with the radial basis function kernel. We map the features into two dimensions using linear discriminant analysis prior to classification. Experimental results are presented.

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Section: Introductionmentioning

confidence: 99%

Multi-scale directional-filtering-based method for follicular lymphoma grading

Bozkurt

Suhre

Çetin

2014

SIViP

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“…The mean brightness values were considered as the threshold value in a pre-segmentation step. Different from the above described techniques, the method in Oger et al (2012) proposed a segmentation of follicular regions on IHC images with registration of the identified regions on H&E images. However, the conformity metric does not reach satisfactory results due to identification of false positive regions.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the threshold values of Dimitropoulos et al (2014) , Luo, Celenk, and Bejai (2006) , Sertel et al (2009) andSertel et al (2008b) were empirically defined and can present disadvantages for practical applications. The methods described by Dimitropoulos, Barmpoutis, Koletsa, Kostopoulos, and Grammalidis (2016) , Sertel et al (2008a) and Oger et al (2012) for segmentation of lymphoma used histological samples stained with IHC and H&E. Using these approaches, different types of images are required for the investigation and segmentation of an abnormality.…”

Section: Related Workmentioning

confidence: 99%

“…In these studies, the training step was composed of a small number of images, and this can be a limiting factor in the representation of ROIs features. Moreover, the methods of Belkacem-Boussaid et al (2011) andOger et al (2012) employed only a few images for the testing step. 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.…”

Section: Related Workmentioning

confidence: 99%

“…These procedures are essential for disease monitoring and more efficient definitions for treatments ( Orlov et al, 2010 ). However, visual evaluation is a complex task due to the significant time involved, its subjectivity and variability between pathologists ( Oger, Belhomme, & Gurcan, 2012;Sertel, Lozanski, Shana'ah, & Gurcan, 2010b ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Review of computer‐assisted diagnosis model to classify follicular lymphoma histology

Saxena,

Aggarwal,

Sinha

et al. 2024

Cell Biochemistry & Function

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The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.

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A general framework for the segmentation of follicular lymphoma virtual slides

Cited by 34 publications

References 18 publications

Multi-scale directional-filtering-based method for follicular lymphoma grading

Multi-scale directional-filtering-based method for follicular lymphoma grading

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

Review of computer‐assisted diagnosis model to classify follicular lymphoma histology

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