scholarly journals Facial image retrieval, identification, and inference system

1993 ◽  
Author(s):  
J. K. Wu ◽  
Y. H. Ang ◽  
P. C. Lam ◽  
S. K. Moorthy ◽  
A. D. Narasimhalu
Keyword(s):  
Image Retrieval ◽  
Facial Image ◽  
Inference System

scholarly journals Mammogram Image Retrieval using Ipso Optimized Anfis Classifier

2019 ◽  
Vol 8 (9S2) ◽  
pp. 799-804
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Noise Removal ◽  
Research Area ◽  
Query Image ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Dataset ◽  
Mammogram Image ◽  
Anfis Classifier

Content-based image retrieval (CBIR) is an research area over the past years that has attracted research. In various medical applications like mammogram analysis CBIR techniques helps the medical team to get similar set of images from a large medical records to help in diagnosis of a disease. This paper proposes an efficient Content-Based Mammogram Image Retrieval method by using an Optimized Classifier. Initially, the input dataset is preprocessed, in which noise removal and contrast enhancement are done. Next, pectoral muscles of the mammogram images are removed using Single Sided Edge Marking (SSEM). Now, feature extraction is done, in which GLCM features, Gabor features and the Local Pattern with Binary features are being removed. The features that are being removed are classified into three classes namely benign, malignant and normal. An optimized classifier named as Adaptive Neuro Fuzzy Inference System (ANFIS), which is optimized by using the Improved Particle Swarm Optimization (IPSO) technique, is used for classification purpose. Finally, similarity is assessed between the trained feature distance vectors and the feature distance vectors of the input query image. Similarity assessment is done using Euclidean Distance metric and the image that has the lowest distance compared with the query is retrieved. The experimental results are obtained for the proposed system and they are compared with the existing techniques.

A Content-Based Approach to Medical Images Retrieval

2013 ◽  
Vol 8 (2) ◽  
pp. 15-27
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Image Retrieval ◽  
Fuzzy Inference ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Content Based Image Retrieval ◽  
Inference System ◽  
Medical Decision Support ◽  
Neuro Fuzzy

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency.

AN INTERACTIVE CBIR SYSTEM BASED ON ANFIS LEARNING SCHEME FOR HUMAN BRAIN MAGNETIC RESONANCE IMAGES RETRIEVAL

2012 ◽  
Vol 24 (01) ◽  
pp. 27-36 ◽  
Author(s):  
Mana Tarjoman ◽  
Emad Fatemizadeh ◽  
Kambiz Badie
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Image Retrieval ◽  
Medical Images ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Medical Decision ◽  
Query Image ◽  
Fuzzy Classifier ◽  
Inference System

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

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