scholarly journals Intelligent Systems to Predict and Diagnose Benign and Malignant Skin Lesions

2020 ◽  
Author(s):  
Geetha C ◽  
Aparna Darapaneni ◽  
Lakkamaneni Chandana Manaswini
Keyword(s):  
Image Processing ◽  
Health Informatics ◽  
Intelligent Systems ◽  
Skin Lesions ◽  
Improve Quality ◽  
Image Processing Techniques ◽  
Malignant Skin ◽  
Processing Software ◽  
Image Processing Software ◽  
Processing Techniques

The main purpose of Intelligent systems is to reason, calculate and perceive relationships and analogies. These Intelligent systems learn from experience and retrieve information from memory and provide the same to the users based onss their requirement. Currently, there is a trend for the use of intelligent systems in health informatics. The main objective of this is to improve quality, efficiency and availability of health services to people round the clock at a lower cost. Intelligent systems aim to predict and diagnose the skin cancer and abrasions based on their images. It understands the cause and thereby analyses the image based on some of the image processing techniques like patterns, anisotropic diffusion, image editing, independent component analysis and image restoration. We make use of image processing software which captures the image and then converts it to digital form and perform the required manipulations.

An Image-Based Method for Automatically Measuring Leaf Parameters with a Scanner

2010 ◽  
Vol 171-172 ◽  
pp. 683-687
Author(s):  
Ai Jun Chen
Keyword(s):  
Image Processing ◽  
Reference Object ◽  
Contour Extraction ◽  
Plant Leaves ◽  
The Real ◽  
Image Processing Techniques ◽  
Processing Software ◽  
Image Processing Software ◽  
Processing Techniques ◽  
Leaf Parameters

A method for measuring parameters of plant leaf is proposed using image processing software developed with Visual C++6.0 based on image processing techniques and a digital scanner. Images with plant leaves and reference objects are acquired using a digital scanner. Then, image segmentation, component labeling and contour extraction are operated and the image parameters(including area, perimeter, length and width) of the leaves and the reference objects in these images are obtained. According to the real parameters and the image parameters of the reference object, two calibration factors can be gained. Finally, the real parameters of plant leaves are detected with the calibration factors and the image parameters of plant leaves. Measuring experiments on three kinds of leaves are performed and experimental results compared with a typical manual method demonstrate that the the proposed method in this paper is efficient and practical.

scholarly journals STITCH PER INCH MEASUREMENT USING IMAGE PROCESSING TECHNIQUES

Arena Tekstil ◽  
2018 ◽  
Vol 33 (2) ◽  
Author(s):  
Andrian Wijayono ◽  
Valentinus Galih Vidia Putra
Keyword(s):  
Image Processing ◽  
Quality Parameters ◽  
Counting Method ◽  
Image Processing Techniques ◽  
Box Counting ◽  
Processing Software ◽  
Different Color ◽  
Box Counting Method ◽  
The One ◽  
Processing Techniques

Stitch per inch is defined as the one of many quality parameters which evaluated in the garment industry. The strength of the stitch will be determined by stitch per inch, both on the clothes and fabrics. Conventionally, stitch per inch determined by using a traditionaly visual method. Many researchers have been developed the image processing technology which applied into various textile fields. In this reserach, it has been developed a new method and a new software which could determine stitch per inch on fabrics using the image processing techniques. Stitch per inch measurement has been done using the box counting method (pixel) on image processing software. Stitch per inch in several fabrics (with different color and structures) has been measured, which shows that the value of the each methods are equal

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.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

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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