scholarly journals A Survey on various image processing techinques for glaucoma diagnosis

2013 ◽  
Vol 5 (1) ◽  
pp. 393-396
Author(s):  
Mrs. Kinjan Chauhan ◽  
Dr. Ravi Gulati
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Image Processing Techniques ◽  
Intra Ocular Pressure ◽  
Glaucoma Diagnosis ◽  
Ocular Pressure ◽  
Processing Techniques

Glaucoma is considered to be the leading cause for blindness in patients. Intra-Ocular Pressure(IOP) is one of theprimary factors which plays an important role in the development of Glaucoma. There is a need for checking progression of Glaucoma in patients at early stage so thats progression can be controlled and/or necessary action can be taken. In this paper, we have surveyed various image processing techniques used for detection of Glaucoma in patients using various opthalmic tests.

SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES

2020 ◽  
Vol 8 (3) ◽  
pp. 01-08
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Weather Condition ◽  
Plant Diseases ◽  
Soybean Plant ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
Soybean Leaf ◽  
Processing Techniques ◽  
Soybean Diseases

Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases. Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease. Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology. Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural. The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm.

scholarly journals Automatic Detection of Optic Disc for Diabetic Retinopathy

2020 ◽  
Vol 9 (7) ◽  
pp. 1013-1015
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Optic Disc ◽  
Early Stage ◽  
Vital Role ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Canny Edge ◽  
Total Blindness ◽  
Processing Techniques

Diabetic Retinopathy affects the retina of the eye and eventually it may lead to total visual impairment. Total blindness can be avoided by detecting Diabetic Retinopathy at an early stage. Various manual tests are used by the doctors to detect the presence of disease, but they are tedious and expensive. Some of the features of Diabetic Retinopathy are exudates, haemorrhages and micro aneurysms. Detection and removal of optic disc plays a vital role in extraction of these features. This paper focuses on detection of optic disc using various image processing techniques, algorithms such as Canny edge, Circular Hough (CHT). Retinal images from IDRiD, Diaret_db0, Diaret_db1, Chasedb and Messidor datasets were used.

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 Plant Disease Detection using Image Processing Techniques

2020 ◽  
pp. 295-300
Author(s):  
Chinmayee Sawant ◽  
Mithila Shirgaonkar ◽  
Sakshi Khule ◽  
Prajakta Jadhav
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Early Stage ◽  
Disease Detection ◽  
Image Processing Techniques ◽  
Affected Plant ◽  
Processing Techniques ◽  
Large Work ◽  
Respective Product ◽  
Automatic Technique

The Indian economy is highly dependent on Agriculture productivity. Having diseases in plants are natural, so disease detection in plant plays an important role in agriculture field. If proper care is not taken, then it causes very serious effects on plants, so that respective product quality and product quantity is affected. Plant disease detection using automatic technique is very useful because it reduces a large work of monitoring in big farms. At very early stage itself it detects the symptoms of diseases when they appear on plant leaves. This project focuses on an approach based on image processing techniques to detect the disease of plants.

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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