2022
DOI: 10.1039/d1na00890k
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Haralick's texture analysis to predict cellular proliferation on randomly oriented electrospun nanomaterials

Abstract: Haralick's texture analysis of the biomaterials was used to assess and predict the cell behaviour on a nanomaterial surface.

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Cited by 5 publications
(3 citation statements)
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“…The texture of a gray-level image can be calculated through Haralick features; therefore, it is possible to correlate these data with the observed biological parameters, namely the number of bacteria in planktonic culture. For each SEM image of the materials without bacteria, we have selected at least two regions of interest (ROIs) to measure the graylevel co-occurrence matrix (GLCM) [39]. Then, for each GLCM, we have calculated one Haralick's feature: the "correlation" [20].…”
Section: Texture Analysis Of Sem Imagesmentioning
confidence: 99%
“…The texture of a gray-level image can be calculated through Haralick features; therefore, it is possible to correlate these data with the observed biological parameters, namely the number of bacteria in planktonic culture. For each SEM image of the materials without bacteria, we have selected at least two regions of interest (ROIs) to measure the graylevel co-occurrence matrix (GLCM) [39]. Then, for each GLCM, we have calculated one Haralick's feature: the "correlation" [20].…”
Section: Texture Analysis Of Sem Imagesmentioning
confidence: 99%
“…Surface texture of scaffolds was studied through two Haralick's surface features (energy and variance) [25], calculated starting from the SEM images obtained via secondary electrons (SEs) at 350× of magnification. This approach evaluates the surface texture using a gray-level image of the surface [26,27]. Briefly, for each SEM image of the scaffold surface, at least two regions of interest (ROIs) were selected to measure the gray-level co-occurrence matrix (GLCM) and then, for each GLCM, Haralick's energy and variance values were quantified.…”
Section: Haralick's Surface Analysismentioning
confidence: 99%
“…The wide range of methods used to investigate surface characteristics, such atomic force microscopy and scanning electron microscopy, can provide important information to enhance our understanding of cell behaviour on biomaterials. In this context, we recently reported that nanotopography-induced changes in cell phenotype and proliferation can be predicted via a computer vision approach [58], the so-called analysis of Haralick's features, where an imaging technique (e.g., SEM) is followed by a texture analysis of the obtained images [59]. Haralick's features are derived from the grey-level co-occurrence matrix (GLCM) and are utilised in many fields such as land-use and forest-type classification [60], fabric defect recognition [61] and in medicine: e.g., skin texture [62], MRI images of the liver [63], X-ray mammography [64], breast cancer [65], brain cancer [66], tumour phenotype [67], and tumour classification [68].…”
Section: Texture Analysis Of Sem Imagesmentioning
confidence: 99%