Document Type : Original Research Paper

Authors

• A. Montazeri ¹
• M. Shamsi ²
• R. Dianat ³

¹ Department of Information Technology, Faculty of Computer, University of Qom, Iran

² Department of Computer, Faculty of Computer and Electrical, Qom University of Technology, Iran

³ Department of Computer, Faculty of Computer, University of Qom, Iran

Abstract

Background and Objective:Image processing is one of the most important issues in the field of artificial intelligence, which is used in various industrial, medical, military, and security systems. One of the most important applications of image processing is the extraction of different types of classification in the field of medical sciences. By using powerful algorithms in this field, intelligent systems can be invented that automatically understand and interpret the medical characteristics of individuals without the need to the physician supervision can discover useful information to help experts make good judgments. When the necessary parameters for the diagnosis of the disease increase, the diagnosis and prognosis of the disease becomes very difficult even for an expert, which is why computer diagnostic tools have been used in recent decades to help the physicians. This has led to a reduction in possible errors due to fatigue or inexperience of the specialist, and to provide the required medical data to the physician in less time and with more detail and accuracy. The purpose of this study is to improve the classification of new methods using a multi-layered model to address retinal diseases diagnosis.
Methods: This paper presents a multi-layer dictionary learning method for classification tasks.  Our multi-layer framework uses a label consistent in K-SVD algorithm to learn a discriminative dictionary for sparse coding in order to learn better features in retinal optical coherence tomography images. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discrimination in sparse codes during dictionary learning process. In fact, it relies on a succession of sparse coding and pooling steps in order to find an effective representation of data for classification. Moreover, we apply Duke dataset for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects 15 normal subjects, 15 AMD patients, and 15 DME patients.
Findings: Our classifier leads to a correct classification rate of 95.85% and 100.00% for normal and abnormal (DME and AMD). Experimental results demonstrate that our algorithm outperforms compared to many recent proposed supervised dictionary learning and sparse representation techniques.
Conlusion: The results of this study were to provide an automatic system for the diagnosis of some retinal abnormalities in a way that it could do data analysis with high accuracy in comparison to other modern methods to diagnosis delicate patterns of OCT, separate images of normal and patient the normal and in two age-related macular degeneration diseases (AMD), and diabetic macular degeneration (DME), and help the physician to diagnose retinal pathology with great care. As a suggestion for professionals and future research, by generalizing this method to the more classes, we can cover the entire retinal myopia and use it as a potentially effective tool in computerized diagnosis and screening for retinal disease or in the wider eye area.

Keywords

• Multi-layer Dictionary Learning
• Sparse Representation
• K-SVD Algorithm
• Optical Coherence Tomography
• Classification

20.1001.1.20080441.1398.13.2.1.4

Main Subjects

• Learning Environment

COPYRIGHTS 
©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.