Texture and Mathematical Morphology for Hot-Spot Detection in Whole Slide Images of Meningiomas and Oligodendrogliomas

Swiderska, Zaneta; Markiewicz, Tomasz; Grala, Bartłomiej; Kozłowski, Wojciech

doi:10.1007/978-3-319-23117-4_1

Cited by 4 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The color components used should allow differentiation between regions such as tumour, hemorrhage/erythrocytes and fold irrespective of the percentage of immunopositive cells. In previous studies [ 12 , 16 ] we observed the significant impact of RGB components on the variability in values of texture characteristics. In paper [ 12 ] we proposed a method to image color representation, independent of the percentage of immunopositive cells.…”

Section: Methodsmentioning

confidence: 77%

“…Only a few steps of the proposed algorithm were presented in the preliminary papers [ 12 – 14 , 16 ]. They were verified on an initial limited data set (10–15 cases) and their parameters were adjusted.…”

Section: Methodsmentioning

confidence: 99%

“…They were verified on an initial limited data set (10–15 cases) and their parameters were adjusted. There were focused on hemorrhage [ 12 ] and specimen fold [ 16 ] detections based on the textural features. Now, we extend our artifact detection machine for a vessel wall elimination procedure.…”

Section: Methodsmentioning

confidence: 99%

“…The Unser textures is a method which is based on the normalized probability applied to the pixel intensity of the image. The modified formulas of Unser features are based on the histograms of the sum and difference of images [ 19 ], which are counted locally in each pixel on examination of its neighborhood region Ω [ 12 , 14 ]. In such a manner, the following features are counted: mean, variance, homogeneity, contrast, energy, correlation, cluster shade and cluster prominence.…”

Section: Methodsmentioning

confidence: 99%

“…Despite the question of quality mentioned above, the problem of the spatial distribution of the selected hot-spot fields is also very compound. Some solutions to the specific tasks of hot-spot selections were presented in our previous studies [ 12 – 14 ]. In this paper, we develop them with the algorithm of specimen fold detection, vessel elimination, small artifact caused error prevention, and the WSI processing strategy to offer a complete system for automatic hot-spot selection in Ki-67 stained meningiomas specimens.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection

et al. 2016

Self Cite

View full text Add to dashboard Cite

BackgroundHot-spot based examination of immunohistochemically stained histological specimens is one of the most important procedures in pathomorphological practice. The development of image acquisition equipment and computational units allows for the automation of this process. Moreover, a lot of possible technical problems occur in everyday histological material, which increases the complexity of the problem. Thus, a full context-based analysis of histological specimens is also needed in the quantification of immunohistochemically stained specimens. One of the most important reactions is the Ki-67 proliferation marker in meningiomas, the most frequent intracranial tumour. The aim of our study is to propose a context-based analysis of Ki-67 stained specimens of meningiomas for automatic selection of hot-spots.MethodsThe proposed solution is based on textural analysis, mathematical morphology, feature ranking and classification, as well as on the proposed hot-spot gradual extinction algorithm to allow for the proper detection of a set of hot-spot fields. The designed whole slide image processing scheme eliminates such artifacts as hemorrhages, folds or stained vessels from the region of interest. To validate automatic results, a set of 104 meningioma specimens were selected and twenty hot-spots inside them were identified independently by two experts. The Spearman rho correlation coefficient was used to compare the results which were also analyzed with the help of a Bland-Altman plot.ResultsThe results show that most of the cases (84) were automatically examined properly with two fields of view with a technical problem at the very most. Next, 13 had three such fields, and only seven specimens did not meet the requirement for the automatic examination. Generally, the Automatic System identifies hot-spot areas, especially their maximum points, better. Analysis of the results confirms the very high concordance between an automatic Ki-67 examination and the expert’s results, with a Spearman rho higher than 0.95.ConclusionThe proposed hot-spot selection algorithm with an extended context-based analysis of whole slide images and hot-spot gradual extinction algorithm provides an efficient tool for simulation of a manual examination. The presented results have confirmed that the automatic examination of Ki-67 in meningiomas could be introduced in the near future.Electronic supplementary materialThe online version of this article (doi:10.1186/s13000-016-0546-7) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 77%