Image Enhancement of Lemon Grasses Using Image Processing Techniques (Histogram Equalization)

2018 ◽  
pp. 298-308 ◽  
Author(s):  
Ofeoritse S. Temiatse ◽  
Sanjay Misra ◽  
Chitra Dhawale ◽  
Ravin Ahuja ◽  
Victor Matthews
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Image Processing Techniques ◽  
Processing Techniques

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

IR and Multi Scale Retinex image Enhancement for Concealed Weapon Detection

2016 ◽  
Vol 1 (2) ◽  
pp. 399 ◽  
Author(s):  
Nashwan Jasim Hussein ◽  
Fei Hu ◽  
Hao Hu ◽  
Abdalrazak Tareq Rahem
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Infrared Images ◽  
Image Processing Techniques ◽  
Multi Scale ◽  
Concealed Weapon Detection ◽  
Contrast Stretching ◽  
Processing Techniques ◽  
Evaluation Techniques

A Concealed Weapon Detection (CWD) had been developed by a large number of researchers and technologies. As a result of the weakness of the infrared images in unique altogether graphic items, infrared and MMW images become inaccurate and insufficient to obviously detectand deal withweaponry objectsin an invisible setting. This article uses Multi Scale Retinex and contrast stretching image processing enhancement techniques to improve the recognition of weapons concealed below attire. Specifically, the focus of the study is on detecting weapons and ammos by enhancing the IR pictures based on image processing techniques. Evaluation techniques were empirically proved to be able to show the enhancement percentage progress.

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 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 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.

scholarly journals Detection of lung Cancer on CT Scan Using Image Processing Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 1-5
Author(s):  
Osman Mudathir ◽  
Alaa Elfadel Kamil ◽  
Suha Salah ◽  
Marwa Gamar ◽  
Zeinab Nouraldaem
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Ct Scan ◽  
Image Enhancement ◽  
Image Processing Techniques ◽  
Important Stage ◽  
Processing Techniques

This paper represents detection of lung cancer using image processing which is followed by image enhancement using three filters. These filters are Gabor, madian and mean filters. Then, image segmentation is applied using a technique called marker controlled watershed with masking that has advantages over other methods in terms of reducing the time needed for detection. On that ground, this method rejoiced with better quality. Finally, an important stage is made to decide whether the lung is infected with cancer or not this stage is called feature extraction .therefore, results were reached with less human efforts.

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