Novel system in vitro of classifying oral carcinogenesis based on feature extraction for gray-level co-occurrence matrix using scanned laser pico projector

In this study, GLCM feature extraction and SLPP reflex images were combined to make a small, non-staining, noninvasive classification system. According to the various image characteristics in oral carcinogenesis, SLPP reflex images better define the borders and three-dimensional structures and provide effective GLCM features such as contrast, energy, and homogeneity to classify carcinogenesis in dysplastic oral keratinocyte (DOK) and normal oral keratinocyte (NOK) cells. Moreover, it also reliably classifies highly metastatic (HSC-3) and tongue cancer (CAL-27) cells. A promising computer-aided classification system for oral cancer was developed to build a reliable intraoral examination system for in situ computer-aided diagnosis in normal clinics.
Source: Lasers in Medical Science - Category: Laser Surgery Source Type: research