The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we analyzed the three-dimensional chromatin texture of cell nuclei based on digital image cytometry. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray level co-occurrence matrices and 3D run length matrices. Finally, to demonstrate the suitability of 3D texture features for classification, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%.

Absfruct -I n this paper, we described breast tissue imagse analyses using texture features from Haar wavelet wavelet transformed images to classify breast lesion of ductal organ Benign, DClS and CA. The approach for creating a classifier is composed of 2 steps: feature extraction and classification. Therefore, in the feature extraction step, we extracted texture features from wavelet transformed images with 10x magnification. In the classification step, we created three classifiers from each image of extracted features using statistical discriminant analysis, neural networks (back-propagation algorithm) and SVM(Support Vector Machines). In this study, we conclude that the best classifier in histological sections of breast tissue in the texture features from second-level wavelet transformed images used in discriminant function.0-7803-8940

Breast cmncer was the most commonly occurring malignancy among women, so study of breast cancer are important. This paper is preprocessing of breast tumor research for segmentation method. There are many thresholding methods. Many thresholding methods developed but they have different result in each image. So we need automatic thresholding method because manual operating is tedious, time-consuming. This study compared result of 6 automatic thresholding method and 1 semisupervised thresholding method in breast tumor image. Otsu's method and Iterative setection are good result in breast tumor cell images. So we expected results using Otsu's method and Iterative Selection tu help for determining and diagnosing the breast tumor. Index Terms-Thresholding Method, Breast Tumor, image Segmentation I. INTRODUCTtONBreast cancer is a malignantrtumor perhaps one might say leads to development of metastatic tumors in women. The recent increase in the incidence of breast cancer in Korean women has highlighted the importance of the study of breast cancer El].Cell segmentation is an important and challenging task in medical image processing 121. Recently, several studies attempted to obtain more objective results using image analysis. In these studies. accurate segmentation is a prerequisite of the success of quantitative analysis [3].Many techniques have been reported for image segmentation. Awasthi [4] used the combination between multiple thresholding, dilation, region growing and binary

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