scholarly journals De-Noising of Uterus Fibroid Ultrasound Image Using Gaussismooth Convolution Filter (GSCF)

2020 ◽  
Vol 5 (7) ◽  
pp. 1033-1037
Author(s):  
Dr. M. Renuka Devi ◽  
V. Sindhu
Keyword(s):  
Image Processing ◽  
Imaging Techniques ◽  
Ultrasound Image ◽  
Cost Effective ◽  
Initial Step ◽  
Threshold Value ◽  
Input Image ◽  
Local Maxima ◽  
Convolution Filter ◽  
Processing Techniques

This paper discusses the methods to detect the presence of uterus fibroid in woman by implementing various image processing techniques. The input image is an ultrasound image as it is cost effective when compared to other imaging techniques like CT, MRI. The initial step in image processing is to remove noise by applying filters. Application of filters smoothen the image without blurring the image. Gradient of the processed image is calculated and the image is enhanced by sharpening the edges of the image are achieved by calculating the local maxima of the gradient. Then, the edges are decided by calculating the threshold value of the processed image. The proposed Gaussismooth Convolution Filter gives better results when compared with other existing filter with PSNR value of 94%.

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 Enhancing ultrasound texture differences for developing an in vivo 'virtual histology' approach to bovine ovarian imaging

2007 ◽  
Vol 19 (8) ◽  
pp. 910 ◽  
Author(s):  
Mark G. Eramian ◽  
Gregg P. Adams ◽  
Roger A. Pierson
Keyword(s):  
Image Processing ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Virtual Histology ◽  
Form And Function ◽  
Non Invasive ◽  
Candidate Image ◽  
And Function ◽  
Processing Techniques

A ‘virtual histology’ can be thought of as the ‘staining’ of a digital ultrasound image via image processing techniques in order to enhance the visualisation of differences in the echotexture of different types of tissues. Several candidate image-processing algorithms for virtual histology using ultrasound images of the bovine ovary were studied. The candidate algorithms were evaluated qualitatively for the ability to enhance the visual differences in intra-ovarian structures and quantitatively, using standard texture description features, for the ability to increase statistical differences in the echotexture of different ovarian tissues. Certain algorithms were found to create textures that were representative of ovarian micro-anatomical structures that one would observe in actual histology. Quantitative analysis using standard texture description features showed that our algorithms increased the statistical differences in the echotexture of stroma regions and corpus luteum regions. This work represents a first step toward both a general algorithm for the virtual histology of ultrasound images and understanding dynamic changes in form and function of the ovary at the microscopic level in a safe, repeatable and non-invasive way.

A Study on Glaucoma Disease Detection with Image Processing Methods

2020 ◽  
Vol 37 ◽  
pp. 25-35
Author(s):  
Shashilata Rawat ◽  
Uma Shankar Kurmi
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Imaging Techniques ◽  
Advanced Imaging ◽  
Detection Techniques ◽  
Glaucoma Detection ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
The Brain ◽  
Aided Diagnosis

The glaucoma is a developing slow eye that effects optic nerve damage in its most common form. Once the optic nerve has been impaired, visual data is not passed to the brain and permanently visual impairment is caused. Glaucoma computer-aided diagnosis (CAD) is a rising area in which medical imaging is analyzed. The CAD is a more precise approach for glaucoma detection, inspired by recent advanced imaging techniques and high-velocity computers. Laser ophthalmoscope scanning, tomography with optical coherence, and retina tomography of Heidelberg have widely used imaging techniques for detecting glaucoma. In this paper, we provide a study of glaucoma disease with its types and detection techniques. Moreover, this paper tells about image processing techniques to detect glaucoma. Variational mode decomposition has also discussed here.

scholarly journals Development of Conventional Controller Based on Image Processing for Monitoring and Controlling Burning Zone Temperature in a Cement Plant in Rotary Kiln Process Through IOT

2021 ◽  
Vol 20 (4) ◽  
pp. 209-214
Author(s):  
Polaiah Bojja ◽  
N. Merrin Prasanna ◽  
Pamula Raja Kumari ◽  
T. Bhuvanendhiran ◽  
Panuganti Jayanth Kumar
Keyword(s):  
Image Processing ◽  
Rotary Kiln ◽  
Continuous Process ◽  
Color Space ◽  
Threshold Value ◽  
Cement Industry ◽  
Raspberry Pi ◽  
Python Language ◽  
Burning Zone ◽  
Processing Techniques

In the cement factories, a rotary kiln is a pyro-processing device that is used to raise the temperature of the materials in a continuous process. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker and it has been implemented using various image processing techniques. In this paper we are measuring and controlling the temperature of rotational kiln in cement industry to get proper clinker ouput. Burning zone flame images are captured using CCD(Charge Coupled Device) camera and are processed using image processing with PID(Proportion Integration and Derivative) controller and which are programmed on raspberry pi card with the help of python language, also the captured images and attributes are transferred to authorized mobile/pc through Raspberry PI by selecting the IP address of mobile or PC. All the attributes received in the mobile in the form of web page the according to the object following data temperature controlled and object is ceaselessly followed to get the proper clinker output. Picture handling calculation with Open cv, as indicated by the calculation the edge estimation of the camera is settled. The frame value of the camera is set. Conversion from RGB color space to HSV color space is achieved and the reference color threshold value is determined. The range esteem estimated by the camera is contrasted and the reference esteem. In this study temp of rotational kiln is measured effectively using PID controller, this controller continuously control the temperature of revolving kiln by varying the i/p images of burning zone at finally fix one flame which is giving 1400degc.

Application of thresholding algorithms for brown bread porosity evaluation

2019 ◽  
Vol 2 (2) ◽  
pp. 99
Author(s):  
Angel Danev ◽  
Atanaska Bosakova-Ardenska ◽  
Miroslav Apostolov
Keyword(s):  
Image Processing ◽  
Threshold Value ◽  
Physicochemical Parameter ◽  
Fast Evaluation ◽  
Real Time Processing ◽  
Global Threshold ◽  
Java Program ◽  
Computer Based ◽  
Brown Bread ◽  
Processing Techniques

The bread is one of the most popular foods in Bulgaria. It’s quality is regulated by approved standards. This paper presents a computer based approach for evaluation of bread porosity which is one of physicochemical parameters of bread quality. The proposed approach includes image processing techniques. A Java program is developed to binarize images of bread and calculate ratio of white pixels to all (coefficient of diversity). This coefficient corresponds with physicochemical parameter- bread porosity. It is used an open-source plugin Auto_Threshold for image binarization. This plugin implements seventeen different algorithms to find global threshold value of a grayscale image. The results show that global thresholding is appropriate for evaluation of bread porosity. The correlation analysis shows that algorithm HisAnalysis could be used for fast and effective evaluation of bread porosity using image processing. Practical applicationsThe use of image processing accelerate the process of bread porosity evaluation. Presented research proves practical benefit to apply image processing for evaluation of physicochemical parameter- bread porosity. The results show that seven algorithms which are included in Auto_Threshold plugin and HisAnalysis algorithm are suitable for bread porosity evaluation. The fastest algorithm is HisAnalysis and it could be used in practice for fast evaluation (in real-time processing) of physicochemical parameter- bread porosity.

A Fastest Patchwise Histogram Construction Algorithm based on Cloud-Computing Architecture

2017 ◽  
Vol 14 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Chung-Chih Cheng ◽  
Fan-Chieh Cheng ◽  
Po-Hsiung Lin ◽  
Wen-Tzeng Huang ◽  
Shih-Chia Huang
Keyword(s):  
Image Processing ◽  
Communication Network ◽  
Cloud Computing ◽  
State Of The Art ◽  
Input Image ◽  
Computation Cost ◽  
Computing Architecture ◽  
Image Processing Techniques ◽  
Construction Algorithm ◽  
Processing Techniques

The histogram in each patch of the input image is a useful feature applied for various development of image processing techniques. However, if the size of the input image is very large, the histogram construction of each patch in the image becomes very time-consuming. For applications involving the processing of several very large images, this paper proposes a superior patchwise histogram construction algorithm based on cloud-computing architecture that is faster than similar state-of-the-art approaches. Through the modern communication network, the computation cost can be easily shared to construct several patchwise histograms at the same time. The proposed algorithm is the fastest solution in the field as well as applicable to various data processing procedures related to probability distribution. Experimental results show that the proposed algorithm has the best performance compared to other related algorithms.

scholarly journals An applied method for railway wheel profile measurements due to wear using image processing techniques

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Hesam Soleimani ◽  
Majid Moavenian ◽  
Reza Masoudi Nejad ◽  
Zhiliang Liu
Keyword(s):  
Image Processing ◽  
Single Channel ◽  
Input Image ◽  
Cross Sectional ◽  
Railway Wheel ◽  
Image Processing Techniques ◽  
Applied Method ◽  
Wheel Profile ◽  
Processing Techniques ◽  
Railway Wheels

AbstractAccurate wear prediction on railway wheels and evolution of railway wheels profile can affect the maintenance planning. The objective of this paper is to provide a new applied method for measuring the railway wheel profile with photographing from the railway wheel to measure by image processing techniques. The aim of this new applied method is to measure the wheel profile using images taken from railway wheels and compare it with the original plan. For this purpose, images were taken by using a camera. In this study, all automatic correction options were turned off and brightness and contrast were in normal conditions. The pixel data is converted to double-type data and are placed in the range of zero and one. Then, the input image which is usually as a three-channel or RGB image is converted to a single channel or gray surface image. Images taken from wheel profiles are processed using image processing techniques. Then the lines, curves and shapes in the image are extracted as cross-sectional and continuous curves. The new applied method results by image processing method obtained show good agreement with those achieved in field measurements.

scholarly journals Brain Tumor Detection by using Fcm Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1856-1862
Keyword(s):  
Image Processing ◽  
Input Image ◽  
Contour Detection ◽  
Optimization Techniques ◽  
Video Frame ◽  
Image Process ◽  
Core Analysis ◽  
Accuracy Level ◽  
Process System ◽  
Processing Techniques

capable of changing a picture into digital type and it perform operations on image. In image process, input is a picture (may be a video frame or a photograph in any format) and therefore the output is also a picture or the characteristics of the input image. Image process system sometimes considers a picture as a 2 dimensional signal, whereas process. It’s one in all the rising technologies, with its branches of application widespread into many domains of business. Image process may be a core analysis in space engineering and it additionally acts as a thrust space in alternative disciplines of applied science. Researchers would like to do perform research in image processing; because it offers real time applications and therefore the results derived from image processing techniques are created. In this paper we have discussed about the greedy snake segmentation, snake contour detection and fcm optimization techniques for segmenting the tumor image, the accuracy level is increased up to 90% compared with the existing algorithm.

scholarly journals Pre-trained classification of scalp conditions using image processing

2020 ◽  
Vol 20 (1) ◽  
pp. 138
Author(s):  
Shafaf Ibrahim ◽  
Zarith Azuren Noor Azmy ◽  
Nur Nabilah Abu Mangshor ◽  
Nurbaity Sabri ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
...  
Keyword(s):  
Image Processing ◽  
Region Of Interest ◽  
Conventional Technique ◽  
Input Image ◽  
Support Vector ◽  
Suitable Treatment ◽  
New Treatment ◽  
Processing Techniques ◽  
Selection Of

<span>Scalp problems may occur due to the miscellaneous factor, which includes genetics, stress, abuse and hair products. The conventional technique for scalp and hair treatment involves high operational cost and complicated diagnosis. Besides, it is becoming progressively important for the payer to investigate the value of new treatment selection in the management of a specific scalp problem. As they are generally expensive and inconvenient, there is an increasing need for an affordable and convenient way of monitoring scalp conditions. Thus, this paper presents a study of pre-trained classification of scalp conditions using image processing techniques. Initially, the scalp image went through the pre-processing such as image enhancement and greyscale conversion. Next, three features of color, texture, and shape were extracted from each input image, and stored in a Region of Interest (ROI) table. The knowledge of the values of the pre-trained features is used as a reference in the classification process subsequently. A technique of Support Vector Machine (SVM) is employed to classify the three types of scalp conditions which are alopecia areata (AA), dandruff and normal. A total of 120 images of the scalp conditions were tested. The classification of scalp conditions indicated a good performance of 85% accuracy. It is expected that the outcome of this study may automatically classify the scalp condition, and may assist the user on a selection of suitable treatment available.</span>

Design and Fabrication of Microfluidics Paper-Based Devices for Contaminant Detection Using aWax Printer

2021 ◽  
Vol 2021 (16) ◽  
pp. 339-1-339-8
Author(s):  
Qiyue Liang ◽  
Min Zhao ◽  
George T. C. Chiu ◽  
Jan P. Allebach
Keyword(s):  
Image Processing ◽  
Microfluidic Device ◽  
Low Cost ◽  
Cost Effective ◽  
Back Side ◽  
Minimum Width ◽  
Before And After ◽  
Wax Printing ◽  
Multiple Devices ◽  
Processing Techniques

In this paper, we introduce an eight-channel paper-based microfluidic device that aims to detect multiple chemicals at once. The microfluidic device we propose is fabricated by wax printing on filter paper, which is trouble-free to handle, low cost, and easy to fabricate. As a hydrophobic material, wax (solid ink) defines the hydrophilic channels for testing. By using image processing techniques, we analyze the width change caused by heating of wax strokes and wax channels, which is a necessary step in the wax printing fabrications. In the same way, we test the minimum width of a channel that allows solutions to cross through and the minimum width of a barrier that is hydrophobic and blocks liquid flow. We also compare two different heating methods, the heat gun and the hot plate, by checking the wax channel width before and after heating based on our image processing pipeline. We conclude that a heat gun will be better for heating channels with relatively large widths. Using high resolution wax printing, we integrate multiple devices on a single paper, which makes this method very cost-effective. Lamination of wax-printed paper based devices is also analyzed, as leakage on the back side of paper is sometimes worth attention.

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