Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas
| Autor: | Hyun-Ju Choi, Heung-Kook Choi, Sang-Hee Nam, Hae-Gil Hwang, Byeong-Il Lee, Hye-Kyoung Yoon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2005 |
| Předmět: |
Cancer Research
Computer science neural network Feature extraction Haar Magnification Breast Neoplasms lcsh:RC254-282 Pathology and Forensic Medicine Breast cancer Wavelet breast cancer Discriminant function analysis medicine Image Processing Computer-Assisted Humans Computer vision lcsh:QH573-671 skin and connective tissue diseases business.industry lcsh:Cytology Discriminant Analysis Pattern recognition Cell Biology General Medicine Ductal carcinoma medicine.disease Linear discriminant analysis lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Carcinoma Intraductal Noninfiltrating Multi-resolution Molecular Medicine wavelet-transformed Female Artificial intelligence Other texture features Neural Networks Computer statistics-based multivariate analysis business |
| Zdroj: | Cellular Oncology, Vol 27, Iss 4, Pp 237-244 (2005) Cellular Oncology : the Official Journal of the International Society for Cellular Oncology |
| ISSN: | 1875-8606 1570-5870 |
| Popis: | Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function. |
| Databáze: | OpenAIRE |
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