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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Kowal, Marek Filipczuk, Pawel Obuchowicz, Andrzej Korbicz, Józef Monczak, Roman |
| Description | Author Affiliation: Kowal M ( Institute of Control and Computation Engineering, University of Zielona Góra, ul. Ogrodowa 3b, 65-246 Zielona Góra, Poland. Electronic address: M.Kowal@issi.uz.zgora.pl.) |
| Abstract | Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96-100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis. |
| File Format | HTM / HTML |
| ISSN | 00104825 |
| Issue Number | 10 |
| Volume Number | 43 |
| e-ISSN | 18790534 |
| Journal | Computers in Biology and Medicine |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2013-10-01 |
| Publisher Place | United States |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Discipline Biology Discipline Biomedical Informatics Biopsy, Fine-needle Breast Neoplasms Pathology Diagnosis, Computer-assisted Methods Microscopy Algorithms Diagnosis Ultrastructure Cell Nucleus Cluster Analysis Female Fuzzy Logic Humans Neural Networks (computer) Reproducibility Of Results Journal Article Research Support, Non-u.s. Gov't |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Computer Science Applications |
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