MORPHOLOGICAL AND TEXTURAL FEATURE EXTRACTIONS FROM FUNGI IMAGES FOR DEVELOPMENT OF AUTOMATED MORPHOLOGY-BASED FUNGI IDENTIFICATION SYSTEM - R. Putri Ayu Pramesti (BRIN - Badan Riset dan Inovasi Nasional, 2022)
Due to widely varied microscopic shapes, fungal classification can be performed based on their morphological features. In morphology-based identification process, feature extraction takes an important role to characterize each fungal type. Previous studies used feature extraction of fungal images to detect the presence of fungal. In this study, morphological and textural features were extracted to classify three types of fungi: Aspergillus, Cladosporium and Trichoderma. Geometry and moment were used as morphological features. To perform textural feature extraction, the local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) feature extraction method were used. We compared the implemented feature extraction methods in order to get the best classification result. The result showed that geometrical features has the accuracy of 65%, higher than that of LBP (60%), GLCM (45%), and moment accuracy (55%). This suggested that geometric features is important for fungal classification based on their morphology.