scholarly journals Image processing techniques for lemons and tomatoes classification

Bragantia ◽  
2008 ◽  
Vol 67 (3) ◽  
pp. 785-789 ◽  
Author(s):  
Antonio Carlos Loureiro Lino ◽  
Juliana Sanches ◽  
Inacio Maria Dal Fabbro
Keyword(s):  
Image Processing ◽  
Classification System ◽  
Fruit Shape ◽  
Morphological Operations ◽  
Border Detection ◽  
Image Processing Techniques ◽  
Operational Costs ◽  
Image Improvement ◽  
Processing Techniques ◽  
Vegetable Quality

Vegetable quality is frequently referred to size, shape, mass, firmness, color and bruises from which fruits can be classified and sorted. However, technological by small and middle producers implementation to assess this quality is unfeasible, due to high costs of software, equipment as well as operational costs. Based on these considerations, the proposal of this research is to evaluate a new open software that enables the classification system by recognizing fruit shape, volume, color and possibly bruises at a unique glance. The software named ImageJ, compatible with Windows, Linux and MAC/OS, is quite popular in medical research and practices, and offers algorithms to obtain the above mentioned parameters. The software allows calculation of volume, area, averages, border detection, image improvement and morphological operations in a variety of image archive formats as well as extensions by means of "plugins" written in Java.

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 77-93 ◽  
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

scholarly journals Pest Detection using Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 1496-1498
Keyword(s):  
Image Processing ◽  
Economic Activity ◽  
Large Scale ◽  
Early Stage ◽  
Productivity Loss ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Pest Detection ◽  
Processing Techniques ◽  
Leaf Image

Agriculture is one of the most significant economic activity. They are many ways that leads to the low productivity of agriculture, but the best method to protect the crop is by detecting the diseases in the early stage. In most of the cases diseases are caused by pest, insects, pathogens which reduce the productivity of the crop at the large scale. If pests are detected on the leaves then, precautions should be taken to avoid huge productivity loss at the end. The main objective of this paper is to identify the pests using image processing techniques like Gaussian blur, segmentation, watershed separation, morphological operations. These techniques are more efficient and less time consuming while identifying the pests over the leaf image with high intensity.

scholarly journals Analysis and automatic segmentation of images for lungs regions extraction in X-ray chest

2020 ◽  
pp. 18-27
Author(s):  
Joel Quintanilla-Domínguez ◽  
Juan Israel Yañez-Vargas ◽  
Miriam Butanda-Serrano ◽  
Enrique Sánchez-Torrecitas
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Morphological Operations ◽  
X Ray ◽  
Image Processing Techniques ◽  
Segmentation Of Images ◽  
Processing Techniques

One of the main disease caused by the COVID-19 in the humans is the pneumonia. This disease mainly attacks the lungs and one of the effective methods for diagnosis is through X-ray chest analysis. Due this in this work a methodology that allow the segmentation and analysis of regions that belong to the lungs in images of X-ray chest is presented. This methodology is based mainly in the implementation of some digital image processing techniques such as: contrast enhancement, segmentation, binarization and the application of morphological operations as the erosion and dilatation.

Efficient Image Denoising for Effective Digitization Using Image Processing Techniques and Neural Networks

2018 ◽  
pp. 1091-1108
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Interesting Problem ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

ICSPI: Intelligent Classification System of Pest Insects Based on Image Processing and Neural Arbitration

2017 ◽  
Vol 33 (4) ◽  
pp. 453-460 ◽  
Author(s):  
Kamil Dimililer ◽  
Salah Zarrouk
Keyword(s):  
Image Processing ◽  
Classification System ◽  
Intelligent Systems ◽  
High Efficiency ◽  
Backpropagation Neural Network ◽  
Classification Rate ◽  
Geometric Shapes ◽  
Image Processing Techniques ◽  
Significant Challenge ◽  
Processing Techniques

Abstract. Detection of insects in agricultural fields is a significant challenge. Minimizing the use of pesticides is necessary for healthier crops and consumers. Therefore, effective and intelligent systems should be designed to fight infestations. This article aims to develop an intelligent insect classification system that would be capable of detecting and classifying the eight insects most commonly found in paddy fields. The developed system comprises two principal stages. In the first stage, the images of the insects are processed using different image processing techniques in order to detect their geometric shapes. The next stage is the classification phase, where a backpropagation neural network is trained and then tested on processed images. Experimentally, the system was tested on different insect images and the results show high efficiency and a classification rate of 93.5%. Keywords: Backpropagation neural networks, Classification, Geometric shapes, Intelligent systems, Pattern averaging, Pest control.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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