Automated system for classifying Hass avocados based on image processing techniques

2014 ◽  
Author(s):  
Edgar Roa Guerrero ◽  
Gustavo Meneses Benavides
Keyword(s):  
Image Processing ◽  
Automated System ◽  
Image Processing Techniques ◽  
Processing Techniques

Neural Networks in Medicine

2011 ◽  
pp. 744-765
Author(s):  
Rajasvaran Logeswaran
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Bile Ducts ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Automated System ◽  
Clinical Test ◽  
Multi Layer Perceptron ◽  
Image Processing Techniques ◽  
Processing Techniques

Automatic detection of tumors in the bile ducts of the liver is very difficult as often, in the defacto noninvasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumors are not clearly visible. Specialists use their experience in anatomy to diagnose a tumor by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumors affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumors, even in the robust clinical test images with other biliary diseases present.

Neural Networks in Medicine

2010 ◽  
pp. 144-165
Author(s):  
Rajasvaran Logeswaran
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Bile Ducts ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Automated System ◽  
Clinical Test ◽  
Multi Layer Perceptron ◽  
Image Processing Techniques ◽  
Processing Techniques

Automatic detection of tumors in the bile ducts of the liver is very difficult as often, in the defacto noninvasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumors are not clearly visible. Specialists use their experience in anatomy to diagnose a tumor by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumors affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumors, even in the robust clinical test images with other biliary diseases present.

Automatic Screening of Diabetic Maculopathy Using Image Processing

2019 ◽  
Vol 15 (4) ◽  
pp. 30-37
Author(s):  
Shweta Reddy
Keyword(s):  
Image Processing ◽  
Feature Detection ◽  
Vision Loss ◽  
Accurate Result ◽  
Screening Method ◽  
Automated System ◽  
Diabetic Maculopathy ◽  
Image Processing Techniques ◽  
Regular Screening ◽  
Processing Techniques

Retinal imaging is a challenging screening method for detection of retinal abnormalities. Diabetic Maculopathy (DM) is a condition that can result from retinopathy. Regular screening is necessary for diabetic maculopathy in order to identify the risk of vision loss. Maculopathy is damage to macula, the key region responsible for high sharp colour vision. Diabetic Retinopathy and Diabetic Maculopathy needs regular observation in order to indicate visual impairment risk. In this article, the author first presents a brief summary of diabetic maculopathy and its causes. Then, an exhaustive literature review of different automated DM diagnosis systems offered. It is important for ophthalmologists to have an automated system which detects early symptoms of the disease and yields a high accurate result. A vital assessment of the image processing techniques used for DM feature detection is projected in this paper. Various methods have been proposed to identify and classify DM based on severity level.

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

Identification of Cucumber Leaf Disease using Image Processing Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 1493-1499
Author(s):  
Shrutika.C.Rampure . ◽  
Dr. Vindhya .P. Malagi ◽  
Dr. Ramesh Babu D.R
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Leaf Disease ◽  
Processing Techniques
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