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. This article is a review of the currently available digital color normalization methods for the bright field histopathology. We describe the proposed color normalization methodologies in detail together with the lesion and tissue types used in the corresponding experiments. We also present the quantitative validation approaches for each of the proposed methodology where available.

a b s t r a c tQuasi-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. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results.

This is the first series investigating tumor-stroma proportion as a prognostic marker in laryngeal carcinomas proposing a new method, but the findings do not support tumor-stroma proportion as a prognostic marker.

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Bu çalışmanın amacı, histoloji yansılarından elde edilmiş gri seviyeli görüntülerde manifold öğrenme yöntemi kullanarak otomatik doku sınıflandırmasını gerçekleştirmektir. Dokulara ait öznitelik vektörleri, lokal birliktelik matrisleri kullanılarak elde edimiş ve öznitelik vektör uzayı boyutları, Đsomap boyut düşürme yöntemi ile azaltılmıştır. Elde edilen düşük boyutlu uzayda, kortamala kümeleme yöntemi ile birbirinden farklı doku kümeleri belirlenmiştir. Bu çalışmada, insan böbreğine ait histoloji yansıları kullanılmıştır. Elde edilen boyutu düşürülmüş öznitelik vektörleri ile doku kümeleri sonuç olarak verilmiştir. Terimler -Doku, birliktelik, Đsomap, boyut düşürme.

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