Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images

Bautista, Pinky A.; Yagi, Yukako

doi:10.1155/2012/501742

Cited by 7 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To acquire the multispectral images of these samples, we used the Olympus microscopic multispectral imaging system (Olympus, Japan) which is equipped with tunable filters which have spectral sensitivities within the visible spectrum. The imaging system also comes with viewer software that enables the user to select sample points from the captured 1434×1050 multispectral images [25]. Each stain component was represented with the average spectrum of the five spectral samples which we extracted from its corresponding multispectral image.…”

Section: Absorption Factors Of the Stain Componentsmentioning

confidence: 99%

Staining Correction in Digital Pathology by Utilizing a Dye Amount Table

Bautista

Yagi

2015

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

The stained colors of the tissue components are popularly used as features for image analysis. However, variations in the staining condition of the histology slides prompt variations to the color distribution of the stained tissue samples which could impact the accuracy of the analysis. In this paper, we present a method to correct the staining condition of a histology image. In the method, a look-up table (LUT) based on the dye amounts absorbed by the sample is built. The LUT can be built when either (i) the source and reference staining conditions are specified or (ii) when the user simply wants to recreate his/her preferred staining condition without specifying any reference slide. The effectiveness of the present method was evaluated in two aspects: (i) CIELAB color difference of nuclei, cytoplasm, and red blood cells, between the ten different slides of liver tissue, and (ii) classification of the different tissue components. Application of the present staining correction method reduced the color difference between the slides by an average factor of 9.8 and the classification performance of a linear discriminant classifier improved by 16.5 % on the average. Results of the paired t test statistical analysis further showed that the reduction in the CIELAB color difference between the slides and the improvement in the classifier's performance when staining correction was implemented is significant at p<0.001.

show abstract

Section: Absorption Factors Of the Stain Componentsmentioning

confidence: 99%

Staining Correction in Digital Pathology by Utilizing a Dye Amount Table

Bautista

Yagi

2015

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Multispectral imaging (MSI) is an optical spectroscopy imaging modality, generating a series of spectral images by capturing the same scene at multiple wavelengths across the spectrum 10 . MSI possesses a distinct capability for characterizing skin due to its ability to leverage the spatial relationships among different tissue absorption spectra within a local region.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing of histopathological staging features of H-E stained human cSCC by Microscopical multispectral Imaging

Yang,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging are crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. How do we improve the diagnosis accuracy and objectivity of pathologists༟Method: We used Multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruijin Hospital. Scale-Invariant Feature Transform is applied to multispectral images for image stitching, while adaptive threshold segmentation method and random forest segmentation method are used for image segmentation, respectively.Results: Synthetic pseudo-color images effectively highlight tissue differences. Quantitative analysis confirms significant variation in nuclear area between normal and cSCC tissues (P < 0.001), supported by an AUC of 1 in ROC analysis. The AUC within cSCC tissues is 0.57. Further study shows higher nuclear atypia in poorly differentiated cSCC tissues compared to well differentiated cSCC (P < 0.001), also with an AUC of 1. Lastly, well differentiated cSCC tissues show more and larger keratin pearls.Conclusion: These results have shown that combined MSI with imaging processing techniques will improve H&E stained human cSCC diagnosis accuracy, and it will be well utilized to distinguish histopathological staging features.

show abstract

“…Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery. Additionally, to solve the problem of different sample staining in different laboratories, Yagi et al proposed a color normalization method based on MSI technology and standard color tiles, improving the accuracy and consistency of diagnosis in different laboratories [10]. Bautista et al stained sections digitally based on the spectral information of different tissues in MSI, which highlight fibrous structures and emphasize the less remarkable structures in hematoxylin and eosin (H&E) stained sections without additional dyeing operations [11].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Recognition of the Pathological Features of Squamous Cell Carcinoma of the Skin Based on Multispectral Imaging

Wang

Chen

Gao

et al. 2022

JCM

View full text Add to dashboard Cite

Cutaneous squamous cell carcinoma (cSCC) is one of the most common skin cancers, a definitive diagnosis of cSCC is crucial to prevent patients from missing out on treatment. The gold standard for the diagnosis of cSCC is still pathological biopsy. Currently, its diagnostic efficiency and accuracy largely depend on the experience of pathologists. Here, we present a simple, fast, and robust technique, a microscopic multispectral imaging system based on LED illumination, to diagnose cSCC qualitatively and quantitatively. The adaptive threshold segmentation method was used to segment the multispectral images into characteristic structures. There was a statistically significant difference between the average nucleocytoplasmic ratio of normal skin (4.239%) and cSCC tissues (15.607%) (p < 0.01), and the keratin pearls cSCC have well-defined qualitative features. These results show that the qualitative and quantitative features obtained from multispectral imaging can be used to comprehensively determine whether or not the tissue is cancerous. This work has significant implications for the development of a low-cost and easy-to-use device, which can not only reduce the complexity of pathological diagnosis but can also achieve the goal of convenient digital staining and access to critical histological information.

show abstract

Multispectral Enhancement Method to Increase the Visual Differences of Tissue Structures in Stained Histopathology Images

Cited by 7 publications

References 21 publications

Staining Correction in Digital Pathology by Utilizing a Dye Amount Table

Staining Correction in Digital Pathology by Utilizing a Dye Amount Table

Distinguishing of histopathological staging features of H-E stained human cSCC by Microscopical multispectral Imaging

Segmentation and Recognition of the Pathological Features of Squamous Cell Carcinoma of the Skin Based on Multispectral Imaging

Contact Info

Product

Resources

About