21-07-2016, 03:31 PM
Abstract
In medical field, the digital images used for diagnostics and therapy are produced in ever increasing quantities. So there is necessity of feature extraction and classification of medical images for easy and efficient retrieval. In this paper, a framework based on Local Tetra Pattern and Fourier Descriptor for content based image retrieval from medical databases is proposed. The proposed approach formulates the relationship between the reference or centre pixel and its neighbours, considering the vertical and horizontal directions calculated using the first-order derivatives. The texture feature of an image is of prime concern; the images filtered by this feature are more appropriate ones as a response to the query image. In this research work, the association of Euclidean Distance(ED) with local tetra pattern is also explored. The proposed framework is successfully tested on standard Messidor dataset of 1200 Retinal images which are annotated with Retinopathy and Macular Edema grades. A tool SS-SVM is applied on binary patterns for endoscopy, dental, skull and retinal images for classification, which results in better classification of images for various dataset, thus improving classifiers.
In medical field, the digital images used for diagnostics and therapy are produced in ever increasing quantities. So there is necessity of feature extraction and classification of medical images for easy and efficient retrieval. In this paper, a framework based on Local Tetra Pattern and Fourier Descriptor for content based image retrieval from medical databases is proposed. The proposed approach formulates the relationship between the reference or centre pixel and its neighbours, considering the vertical and horizontal directions calculated using the first-order derivatives. The texture feature of an image is of prime concern; the images filtered by this feature are more appropriate ones as a response to the query image. In this research work, the association of Euclidean Distance(ED) with local tetra pattern is also explored. The proposed framework is successfully tested on standard Messidor dataset of 1200 Retinal images which are annotated with Retinopathy and Macular Edema grades. A tool SS-SVM is applied on binary patterns for endoscopy, dental, skull and retinal images for classification, which results in better classification of images for various dataset, thus improving classifiers.
