Image analysis of immunohistochemical stains for detection of parathyroid disease

In this paper a method is introduced which enables automatic detection of parathyroid hyperplasia and parathyroid adenoma on the basis of immunohistochemical angiogenesis markers expression in micrographs. The proposed method uses digital image processing techniques and classification algorithms to detect diseased tissue. The disease detection is performed by classification of normalized color intensity histograms. Accuracy of this method was evaluated by using micrographs of parathyroid tissue sections obtained from patients that have undertaken surgery due to primary hyperparathyroidism. Use of different color models, various classifiers, and immunohistochemical markers was considered during the experiments. The experimental results show that the introduced method enables accurate detection of parathyroid disease. The most promising results were obtained for k-nearest neighbor and neural network classifiers

Detection of immunogold markers in images obtained from transmission electron microscopy

In this paper a method is introduced which enables automatic detection of immunogold markers in transmission electron micrographs. Immunogold markers are used in electron microscopy to determine sub-cellular location of biological relevant macromolecules, such as proteins, lipids, carbohydrates, and nucleic acids. The proposed method combines image segmentation and feature localization approaches to improve accuracy of the immunogold markers detection in low contrast and highly textured image regions. A segmentation algorithm is intended in this study, which applies a flood-fill morphological operation. Accuracy of this method was evaluated by using electron microscopy images of human colorectal carcinoma cells. The experimental results show that the introduced method enables detection of immunogold markers with low false positive and false negative rates