scholarly journals Detection of Cataract Through Feature Extraction by the Novel Angular Binary Pattern (NABP) and Classification by Kernel Based Convolutional Neural Networks

2021 ◽  
Author(s):  
A Sirajudeen ◽  
Anuradha balasubramaniam ◽  
S Karthikeyan
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Texture Features ◽  
Processing Technique ◽  
Image Processing Technique ◽  
The Novel ◽  
Shape Features ◽  
Visual Interpretation ◽  
Color Features

Abstract Cataract is a condition of the opacity in the lenticular regions, which usually results in bad visual interpretation of the viewed object or any entity. Hence the timely detection of cataract is considered to be significant and can even contribute in the prevention from loss of fight that might occur if the cataract is left untreated. In this paper, detection of cataract disease is carried out based on the image processing technique. Color features, texture features and shape features are extracted separately. This study proposed a Novel Angular Binary Pattern (NABP) for the extraction of texture features. And after the extraction of features, the images are subjected to classification through the implementation of the proposed novel Kernel Based Convolutional Neural Networks. Results are obtained separately for all the three types of features. A comparison is carried out for the proposed work with existing works and based on the results obtained it can be seen that the proposed work comes up with the enhanced results than the traditional methods.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Nondestructive Evaluation System for White Ginseng Quality Using Image Processing Technique

2006 ◽  
Vol 321-323 ◽  
pp. 1225-1228
Author(s):  
Seong Min Kim ◽  
Chul Soo Kim ◽  
Chong Ho Lee ◽  
Myung Ho Kim ◽  
Seung Jae Park
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Evaluation System ◽  
Image Acquisition ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Gray Level ◽  
White Ginseng ◽  
Artificial Neural

A real-time white ginseng quality evaluation system based on a machine vision technique and artificial neural networks was developed to replace the current manual grading and its efficiency was tested. The system consisted of conveyor, image acquisition system synchronized with a sample-detecting sensor, and image processing and decision-making system. Software running under Windows system was developed. The algorithm included three consecutive stages of (a) image acquisition and preprocessing, (b) mathematical feature extraction, and (c) grade decision using artificial neural networks. Mathematical features such as area ratio, mean and standard deviation of gray level, skewness of gray level histogram, and the number of run segment, were extracted from five equally divided parts of a specimen. An artificial neural network model was used to classify samples into three grading categories. The grading error of the system was about 26%, which is comparable to the 30% in case of manual grading. The grading rate was one sample per a second.

scholarly journals Cervical Cancer Cell Identification & Detection Using Fuzzy C Mean and K nearest Neighbor Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 1080-1084
Keyword(s):  
Cervical Cancer ◽  
Nearest Neighbor ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Shape Features ◽  
K Nearest Neighbor ◽  
Learning Skills ◽  
Color Features ◽  
High Level ◽  
Morphology Changes

Across the globe, woman has been diagnosed two major forms of cancer, in which one is identified as cervical cancer and its micro classification. Morphology changes in cells or dead nucleus in the cervix causes cervical cancer. These cells are characterized with multiple nucleuses, faulty & lack of cytoplasm and so on. Detection of cervical cancer using smear test is extremely challenging because such cells does not offer texture variations or any significant color from the normal cells. Therefore for identification in abnormality of cells we required high level Digital image processing technique which compromises an automated, comprehensive machine learning skills. An advanced Fuzzy based technique has been implied to separate nucleus and cytoplasm from the cell. KNN is instructed with the color features and shape features of the segmented units of the cell and then an unknown cervix cell samples are classified by this technique. The proposed technique gives shape and color features of nucleus and cytoplasm of the cervix cell.

A New Image Processing Technique for STEM

1974 ◽  
Vol 32 ◽  
pp. 432-433
Author(s):  
Yasushi Kokubo ◽  
Hirotami Koike ◽  
Teruo Someya
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Elastic Scattering ◽  
Field Emission ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Energy Analyzer ◽  
Scanning Microscope ◽  
Scanning Electron

One of the advantages of scanning electron microscopy is the capability for processing the image contrast, i.e., the image processing technique. Crewe et al were the first to apply this technique to a field emission scanning microscope and show images of individual atoms. They obtained a contrast which depended exclusively on the atomic numbers of specimen elements (Zcontrast), by displaying the images treated with the intensity ratio of elastically scattered to inelastically scattered electrons. The elastic scattering electrons were extracted by a solid detector and inelastic scattering electrons by an energy analyzer. We noted, however, that there is a possibility of the same contrast being obtained only by using an annular-type solid detector consisting of multiple concentric detector elements.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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