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OBJECTIVE: Nucleolar organizer regions are loops of DNA containing ribosomal RNA genes and presumably are associated with ribosomal RNA activity, protein synthesis, and cell proliferation. Argyrophilic nucleolar organizer region (AgNOR) count has been suggested as an objective method in differentiating dysplastic lesions from non‑dysplastic lesions. MATERIALS AND METHODS: This descriptive study was done on archival paraffin blocks (n = 60), consisting of 10 normal human oral epithelium, 22 cases of non‑dysplastic leukoplakia (NDLK), and 28 cases of dysplastic leukoplakia (DLK). The AgNORs were counted with the aid of a manual using conventional light microscopy and photographs of the same were taken and analyzed using Image Pro Express 6.0 (Media Cybernetic Inc., USA) for windows. RESULTS: The mean AgNOR count per nucleus was found to be higher in patients with DLK as compared to NDLK and controls using both manual counting and image analysis method and on comparing both the techniques, image analysis provide a more accurate reflection of AgNOR counts than manual counting. CONCLUSION: To conclude, reliability of computerized image technique of AgNOR count is the most appropriate marker to differentiate between dysplastic and NDLK. Computer‑assisted image analysis system was found to be an effective tool in achieving high reproducibility as compare to manual.
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0.05).But there were obvious differences between each electrically injured groups and each burned groups(P
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The computerized image analysis is a useful tool for the quantitative assessment of histopathologic findings. In contrast to the usual microscopic examination by pathologists, the computerization should be accompanied with the standardization process of the image. We developed an algorithm to standardize images and to determine the optimal gray level threshold, using a myocardial fibrosis model. Sirius red staining was more convenient for the image analysis than Masson's trichrome staining because of a better contrast with the surrounding structures. To get an optimal measurement, light intensity was standardized at each of the fibrosis, myocardium and background. In this study, the most promising method to determine the degree of fibrosis was that of revising the background without tissue to a gray level of 200, obtaining a green component of the color image, revising the myocardial fiber to 163, and defining a partial ratio as fibrosis index when the gray level threshold was 120. These threshold levels and parameters were determined after drawing the binarization index curves according to the change of the gray level threshold and by the morphological examination of the actual binarization figures overlaid to the original color image. Through these processes we could get a consistent result on the myocardial fibrosis and we expect a similar principle applies when we analyze color images in the histopathologic quantitation by computerized image analysis.
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Fibrose , MiocárdioRESUMO
BACKGROUND: Nucleolar organizer regions(NORs) are loops of DNA that transcribe ribosomal RNA; they can be easily identified in paraffin section using silver method. Recently, the application of NORs to the study of various types of benign and malignant proliferation has suggested that the number of NORs per nucleus can provide an indication of degree of cellur maligriancy. Many authors indicated a significant overlapping of NORs counts between benign and malignant proliferation. OBJECTIVE: The purpose of this study is to discriminate benign m laocytic nevi from malignant melanoma effectively, thus we use simple enumeration and computerized image analysis of AgNORs in melanocytic skin lesions. METHOD: NORs were investigated on silver stained histologicec ion of 18 malignant melanoma (MM), 19 acquired nevi(AN), 21 congenital nevi(CN). For each case, 100 nucleus sample were cuantified using image analysis system(AIC Inc., Roswell, GA). There is no consensus at present as to the best criterion for quantifying AgNORs proteins in melanocytic skin lesion. We prefer to quantify a large number of parameters. The mean value or standard deviation(SD), each calculated for a batch of 100 cells, are average again(mean+SD), over the 58 cases for six morphological criteria mean number of AgNORs per nucleu, mean ratio of AgNORs area per ruc eus area, mean area of largest AgNORs, mean ratio of largest AgNORs area per nucleus area, mean nucleus area per a AgNOR, and coefficient of variation of nucleus area. RESULTS: 1. All of the six parameters show significant difference between benign melanocytic nevi and malignant melanoma. But., there is no significant difference between acquired nevi and congenital nevi. 2. In stepwise discriminate analysis, we discriminate benign melanoctic nevi from malignant melanoma effectively using combined two parameters(number of AgNORs and mean ratio of AgNORs area per nucleus area). But there is some overlapping between malignant melanoma and ber ign melanocytic nevi using each six parameters. 3. In metastatic and non-metastatic malignant melanoma, six param ters have not shown significant difference. CONCLUSION: Study of AgNORs in the image analysis system is a very useful tool to differentiate malignant melanoma from benign melanocytic nevi.