In English

Presentationsinformation     2013-11-11 (14:15)   •  The seminar room at Vi2

Talare Sajith Kecheril Sadanandan
Titel Automated lung cancer detection using scale space features
Sammanfattning The presentation aims at development of a sputum cytology image analysis system which identifies benign and malignant glandular cells. In our proposed system, we developed an automated lung cancer detection system which segments the cell nuclei and classifies the glandular cells from the given sputum cytology image using a novel scale space catastrophe histogram (SSCH) feature. Catastrophe points occur when pairwise annihilation of extrema and saddle happens in scale space. Unusual nuclear texture shows the presence of malignancy in cells, and SSCH-based texture feature extraction from nuclear region is done. From the input high-resolution image, the cellular regions are localized using maximization of determinant of Hessian, nuclei regions are segmented using K-means clustering, and SSCH features are extracted and classified using support vector machine and color thresholding. The experimental results show that the proposed method obtained an accuracy of 87.53% which is better than Gabor filter-based gray-level co-occurrence features, local binary pattern, and complex Daubechies wavelet based features. The results obtained are in accordance with the dataset classified by medical experts.