Adaptive fuzzy inference system based directional median filter for impulse noise removal

2016 ◽  
Vol 70 (5) ◽  
pp. 689-697 ◽  
Author(s):  
Muhammad Habib ◽  
Ayyaz Hussain ◽  
Saqib Rasheed ◽  
Mubashir Ali
Keyword(s):  
Fuzzy Inference System ◽  
Impulse Noise ◽  
Fuzzy Inference ◽  
Median Filter ◽  
Noise Removal ◽  
Inference System ◽  
Adaptive Fuzzy ◽  
Impulse Noise Removal

scholarly journals Liver Lesions Diagnosis Using Gwo-Anfis Framework

2019 ◽  
Vol 8 (6) ◽  
pp. 3692-3696
Keyword(s):  
Neural Network ◽  
Impulse Noise ◽  
Fuzzy Inference ◽  
Texture Feature ◽  
Noise Removal ◽  
Classification Model ◽  
Gray Wolf ◽  
Liver Lesions ◽  
Inference System ◽  
Impulse Noise Removal

In this paper, liver abnormality is detected using an improved classification model that consists of series of process. The study reveals the liver condition to be normal or abnormal using the proposed system. The study uses both structural and statistical analysis, where both these analysis is combined with the process of classification. Initially, the noises are removed using Impulse Noise Removal and then the Segmentation is carried out using Gray Wolf Optimisation (GWO) algorithm. After the segmentation, the features are extracted through Local Binary Patters (LBP) Operator and then Artificial Neural Network Fuzzy Inference System (ANFIS) classifies the liver regions as malignant or benign. Various images collected from laboratories are used in both training and testing stages. The results are validated in terms of two different texture feature extractors namely, GLCM and LBP. The result shows that the proposed classifier using GLCM classifier obtains improved classified patterns than the existing methods.

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