Color Correction of Stained Tissue Section Images by Histogram Transfer According to Control Images.

Objective: To present a color correction method by histogram transfer depending upon control tissue image (CTI) differences, and subsequently evaluate its performance.

Study Design: Images from colon and placenta sections stained by anti-CD34 were used as CTI and/ or sample tissue images (STIs). In total, 36 slides were stained: 1 according to standard procedure and 35 with some variation in the durations or dilutions used for the staining process.

Automated labeling of cancer textures in larynx histopathology slides using quasi-supervised learning.

Objective: To evaluate the performance of a quasi-supervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues.

Study Design: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions.

A review on color normalization and color deconvolution methods in histopathology.

The histopathologists get the benefits of wide range of colored dyes to have much useful information about the lesions and the tissue compositions. Despite its advantages, the staining process comes up with quite complex variations in staining concentrations and correlations, tissue fixation types, and fixation time periods. Together with the improvements in computing power and with the development of novel image analysis methods, these imperfections have led to the emerging of several color normalization algorithms.

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma.

The prognostic value of tumor-stroma proportion in laryngeal squamous cell carcinoma.

Objective: Tumor-stroma proportion of tumor has been presented as a prognostic factor in some types of adenocarcinomas, but there is no information about squamous cell carcinomas and laryngeal carcinomas.

Material And Method: Five digital images of the tumor sections were obtained from 85 laryngeal carcinomas. Proportion of epithelial tumor component and stroma were measured by a software tool, allowing the pathologists to mark 205.