Smart Cataract Detector

2020 ◽  
pp. 246-262
Author(s):  
M. Manikandakumar
Keyword(s):  
Image Processing ◽  
Mobile Technology ◽  
Mobile Application ◽  
Smart Phone ◽  
Eye Disease ◽  
Poor People ◽  
Eye Injuries ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Smart Phone Application

Eyes react to light. Cataract, a common eye disease, clouds the lens of the eye, which decreases vision and can cause blindness. It can be in one or both eyes. The most major cause of cataract is ageing. Cataract also can be found in children and can occur due to eye injuries and inflammation. As mobile technology emerges to a greater extent, designing an application would help people perform a self check-up without meeting the doctors in person, thus saving time and cost. The pre-diagnosis of cataracts is costly for poor people. Time taken by the machine to detect cataract is greater, so the doctor can't attend as many patients. A cheap, reliable mobile application that helps diagnose cataract would cut cost and simplify diagnosis. This chapter proposes the development of smart phone application to detect the presence of cataract using image processing techniques.

scholarly journals Nitrogen Deficiency Mobile Application for Rice Plant through Image Processing Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 2950-2955
Keyword(s):  
Image Processing ◽  
Mobile Application ◽  
Nitrogen Deficiency ◽  
Assistive Technologies ◽  
Blue Color ◽  
Leaf Color ◽  
Image Processing Techniques ◽  
Rice Plants ◽  
Digital Devices ◽  
Processing Techniques

Driven by the opportunity that digital devices and robust information are readily available, the development and application of new techniques and tools in agriculture are challenging and rewarding processes. This includes techniques learned that is based on traditional methods, practices, experiences, environmental patterns and human capability. The most sought technique comes from human intelligence that is dynamic, adaptive and robust. Nitrogen deficiency in rice plants can be determined via the color of the leaves. It is dependent on the depth of the green pigment in the color spectrum present in the leaves. Based on these characteristics, the application of computational artificial intelligence and machine vision can be adopted to create assistive technologies for agriculture. In this paper, a mobile application is developed and implemented that can be used to assist rice farmers determine nitrogen deficiency, through the leaf color in rice plants. The application can be used alternatively or together with the traditional protocol of nitrogen fertilizer management. It is mobile, simple and it also addresses some drawbacks of the human eye to distinguish color depths brought about by other factors, like sunlight, shading, humidity, temperature, etc. It utilizes image processing techniques to digitally captured images represented in numerically transformed Red, Green, and Blue color formats. The digital images are then normalized to remove the effects of illumination and then compared using the image/pixel subtraction technique with the base color images converted and extracted from the leaf color chart standard. Eventually, the application determines nitrogen deficiency and suggests the concentration and volume of fertilizer to be applied to the rice plants. Accuracy of the technique is determined by computing the Z statistic score.

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.

Rice Panicle Blast Detection and Grading Based on Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 165-168 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Swaraj Kumar Sahu ◽  
Nalini Kanta Barpanda ◽  
Amiya Kumar Rath
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Panicle Blast ◽  
Processing Techniques
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