Lina Mirić

Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images

This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude.

Matrix metalloproteinases and E-cadherin immunoreactivity in different basal cell carcinoma histological types

The immunohistochemical staining of matrix metalloproteinases (MMPs) and E-cadherin in tumor epithelial and stromal cells was analyzed in a group of solid, superficial spreading and cystic tumors and in a group of morpheaform and recurrent basal cell carcinomas (BCC) in order to determine whether any of these factors possibly contribute to tumor therapy resistance. Tumor tissues of 64 patients were obtained by complete excisional or curettage biopsy of BCC and these were immunohistochemically stained for MMP-1, MMP-2, MMP-9, MMP-13 and E-cadherin. In the morpheaform and recurrent BCC, MMP-9 expression significantly increased in the stroma, while E-cadherin expression was negative in epithelial cells. Odds ratio for development of morpheaform and recurrent BCC was 6.2 for positive MMP-1 immunostaining in epithelial tumor cells, 5.8 for positive MMP-9 immunostaining in tumor stroma, 3.2 for positive MMP-13 immunostaining in tumor stroma, and 4.5 for negative E-cadherin in epithelial tumor cells. Our results suggest that MMP-1 immunostaining in tumor cells, MMP-9 expression in stromal cells, and absence of E-cadherin expression are associated with morpheaform and recurrent BCC.

Dependent component analysis based approach to robust demarcation of skin tumors

Method for robust demarcation of the basal cell carcinoma (BCC) is presented employing novel dependent component analysis (DCA)-based approach to unsupervised segmentation of the red-green-blue (RGB) fluorescent image of the BCC. It exploits spectral diversity between the BCC and the surrounding tissue. DCA represents an extension of the independent component analysis (ICA) and is necessary to account for statistical dependence induced by spectral similarity between the BCC and surrounding tissue. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization and ICA we experimentally demonstrate good performance of DCA-based BCC demarcation in demanding scenario where intensity of the fluorescent image has been varied almost two-orders of magnitude.