Fuzzy Logic for Image Processing: Definition and Applications of a Fuzzy Image Processing Scheme

Aim: To design diagnostic expert system using fuzzy image processing for diabetic retinopathy, measures diabetic eye morbidity. Method: From this research paper, diagnosing diabetic retinopathy using fuzzy image processing for diabetic patients. Firstly collection of OCT images of the patient who has diabetic retinopathy. Author’s proposed method finds out the edge detection of the OCT image. Then fuzzy logic is applied on that result of image processing. Design a fuzzy rules and input- output parameter. This method gives accurate diagnosing the diabetic retinopathy from the image of the patient’s retina images. Result: This diagnostic system gives patient’s eye morbidity, vision threatening of the diabetic patients. In the result, edges of the retina images, and from that retinal ruptures, thickness of the proliferative in the retina. From these result, diagnostic of diabetic retinopathy conditions such as PDR, NPDR, and NORMAL, and CSME in the diabetic patients. Conclusion: author has design diagnostic system for endocrinologist and ophthalmology to diagnosed diabetic retinopathy in the patients. From this system doctors don’t need patients for diagnosing purposed.

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Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

PLoS ONE ◽

10.1371/journal.pone.0251899 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251899

Author(s):

Samir M. Badawy ◽

Abd El-Naser A. Mohamed ◽

Alaa A. Hefnawy ◽

Hassan E. Zidan ◽

Mohammed T. GadAllah ◽

...

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Deep Learning ◽

Tissue Characterization ◽

Semantic Segmentation ◽

Batch Processing ◽

Ultrasound Images ◽

Processing Scheme ◽

Processing Mode ◽

Small Dataset

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

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Edge detection technique by fuzzy logic and Cellular Learning Automata using fuzzy image processing

2013 International Conference on Computer Communication and Informatics ◽

10.1109/iccci.2013.6466130 ◽

2013 ◽

Cited By ~ 12

Author(s):

Dhiraj Kumar Patel ◽

Sagar A. More

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Edge Detection ◽

Learning Automata ◽

Detection Technique ◽

Cellular Learning Automata ◽

Fuzzy Image

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Digital Image Processing for Detecting and Classifying Plant Diseases

Circulation in Computer Science ◽

10.22632/ccs-2017-252-66 ◽

2017 ◽

Vol 2 (11) ◽

pp. 1-7

Author(s):

Izay A. ◽

Onyejegbu L. N.

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Programming Language ◽

Design Methodology ◽

Fuzzy Logic Controller ◽

Object Oriented ◽

Plant Diseases ◽

Analysis And Design ◽

Crop Diseases ◽

Growing Population

Agriculture is the backbone of human sustenance in this world. With growing population, there is need for increased productivity in agriculture to be able to meet the demands. Diseases can occur on any part of a plant, but in this paper only the symptoms in the fruits of a plant is considered using segmentation algorithm and edge/ sizing detectors. We also looked at image processing using fuzzy logic controller. The system was designed using object oriented analysis and design methodology. It was implemented using MySQL for the database, and PHP programming language. This system will be of great benefit to farmers and will encourage them in investing their resources since crop diseases can be detected and eliminated early.

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Object Detection Robot Using Fuzzy Logic Controller Through Image Processing

Journal of Physics Conference Series ◽

10.1088/1742-6596/1737/1/012045 ◽

2021 ◽

Vol 1737 (1) ◽

pp. 012045

Author(s):

M Khairudin ◽

S Yatmono ◽

AC Nugraha ◽

M Ikhsani ◽

A Shah ◽

...

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Object Detection ◽

Fuzzy Logic Controller

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UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10i4.17023 ◽

2017 ◽

Vol 10 (4) ◽

pp. 32

Author(s):

Srinivasan A ◽

Sudha S

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Diabetic Retinopathy ◽

Characteristic Curve ◽

Area Under The Curve ◽

Computation Time ◽

Automated System ◽

Morphological Operations ◽

Advanced Stage ◽

Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.

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