scholarly journals Microcomputers in an Introductory College Astronomy Laboratory: A Software Development Project

1990 ◽  
Vol 105 ◽  
pp. 175-176
Author(s):  
David D. Meisel ◽  
Kenneth F. Kinsey ◽  
Charles H. Recchia
Keyword(s):  
Image Processing ◽  
Histogram Equalization ◽  
Development Project ◽  
Radial Coordinate ◽  
Image Processing Techniques ◽  
Software Development Project ◽  
Low Pass ◽  
Data Points ◽  
College Astronomy ◽  
Processing Techniques

We have developed software for the Apple IIe series of microcomputers for use in labs in an introductory astronomy course. This software emphasizes a toolkit approach to data analysis; it has been class tested with over 170 students and was a resounding success as a replacement for previously used graphical approximations. A unique feature of this software is the incorporation of image-processing techniques into a course designed for non-science majors.The five software packages are:(a)Datasheet - A six-column spreadsheet with columnwise operations, statistical functions, and double-high-resolution graphics.(b)Image-Processor Program - Allows 37 × 27 pixel × 8 bit video captured images to be manipulated using standard image-processing techniques such as low pass/high pass filtering and histogram equalization.(c)Picture-Processor Program - Allows 256 × 192 bilevel pictures to be manipulated and measured with functions that include calipers, odometer, planimeter, and protractor.(d)Orrery Program - Simulates planet configurations along the ecliptic. A movable cursor allows selection of specific configurations. Since both relative times and angular positions are given, students can deduce the scale of the solar system using simple trigonometry.(e)Plot Program - Allows orbital positions as observed from above the pole to be plotted on the screen. By entering trial values of elliptical orbit parameters, students obtain and the program plots the best fitting ellipse to the data. The sum of the squares of the residuals in the radial coordinate is given after each trial so that students can discover convergence more easily than by simple visual examination of a plot comparing the trial theoretical points with the raw data points.

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

scholarly journals Skin cancer detection and stage prediction using image processing techniques

2018 ◽  
Vol 7 (1.8) ◽  
pp. 204 ◽  
Author(s):  
Sheeju Diana ◽  
Ramamurthy B
Keyword(s):  
Image Processing ◽  
Skin Cancer ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Noise Removal ◽  
The Body ◽  
Back Propagation Algorithm ◽  
Environmental Threats ◽  
Image Processing Techniques ◽  
Processing Techniques

Skin cancer is one of the perilous forms of cancer that most recently occurred in preceding and in recent years as well. Early detection of skin cancer is curable and it eliminates the cost that is spent on the advanced treatment. Skin cancer mainly occurs due to exposure to sun’s ultraviolet radiation and other environmental threats. It can be categorized into, Melanoma and Non-Melanoma. Melanoma is dangerous one. Once it is occurred it starts spreading across other parts of the body if not treated in the early stages. Non-Melanoma is a static cancer which does not affect the normal cells of the skin. This paper aims to develop an application to detect skin cancer and stage prediction using Image Processing Techniques. Stage is predicted, so that the treatment for the same is done without any delay. Skin cancer affected image is taken as input and various preprocessing techniques is applied for the same. The Preprocessing Techniques such as Noise Removal is applied on the image to filter out the noise. Filtered image is enhanced using Histogram Equalization and image is segmented to extract the affected portion. The Area, Perimeter and Eccentricity values are calculated for the affected portion of the skin. The values are then fed into the Neural Networks using Back Propagation algorithm in order to predict the Stage and type of the Skin cancer.

scholarly journals Segmentasi Thresholding untuk Pemilihan Kualitas Telur Asin

2018 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Oky Dwi Nurhayati ◽  
Diana Nur Afifah ◽  
Nuryanto . ◽  
Ninik Rustanti
Keyword(s):  
Image Processing ◽  
Software Design ◽  
Good Accuracy ◽  
Histogram Equalization ◽  
Poor Quality ◽  
Image Processing Techniques ◽  
First Order ◽  
Egg Shells ◽  
Processing Techniques

Visually, choosing the quality of salted eggs by looking at egg shells is something that is very difficult to do. In addition, the lighting and the weakness of the senses of vision also becomes difficult to see the quality of salted eggs visually. So far, to determine a good salted egg, only known from the weight of eggs. Not all eggs that have mild density have poor quality. So far, suppliers often get eggs that have bad quality (broken) so that when processed will produce defective salted eggs. The goal achieved as an effort to improve the quality of this production is software design to know the quality of salted eggs. Quality selection technology involves image processing techniques such as gray imagery, histogram equalization, P-Tile segmentation, and first-order statistical feature extraction that serves to recognize the type of egg image quality. The results obtained with the application of image processing techniques have a fairly good accuracy to determine the quality of salted eggs into two good and bad conditions.

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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