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.

Implications of Digital Image Processing in the Paraclinical Assessment of the Partially Edentated Patient

2018 ◽  
Vol 69 (2) ◽  
pp. 521-524
Author(s):  
Magda Ecaterina Antohe ◽  
Doriana Agop Forna ◽  
Cristina Gena Dascalu ◽  
Norina Consuela Forna
Keyword(s):  
Image Processing ◽  
Image Enhancement ◽  
Treatment Plan ◽  
Digital Processing ◽  
Contour Detection ◽  
Anatomical Structures ◽  
Imaging Processing ◽  
Point To Point ◽  
Radiological Image ◽  
Processing Techniques

The application of certain digital processing techniques offers the possibility of extra accuracy in the interpretation of paraclinical examinations of this type, with profound implications in the diagnosis as well as in the hierarchy of the treatment plan. The purpose of this study is to identify the type of imaging processing for the identification of pathological elements from orthopantomographies and articular tomographies. A number of 20 orthopantomographies and 15 temporo-mandibular joint tomography have undergone through various image enhancement techniques. Various methods of image enhancement (enhancement) have been used for those procedures whereby it becomes more useful in the following aspects: specific details are highlighted; noise is eliminated; the image becomes more visually attractive. The workings were done in Corel PhotoPaint 7.0, using the automatic procedures available.The choice of a particular type of image enhancement technique has been selected for each type of pathology found in orthopantomographies or articular tomography, providing the best accuracy for an optimal imaging interpretation that underpins a precision diagnosis.Of the most useful imaging processing in the optimization of the orthopantomographic image accuracy the point-to-point transformations are to be noted. The image processing proposed in this article focused primarily on improving the radiological image attributes to highlight specific anatomical structures, and secondly, the contour detection, where it was necessary for the diagnostic purposes as well.

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

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.

Design of a High-Reliability FPGA Platform for High-Resolution Image Process System

2015 ◽  
Vol 764-765 ◽  
pp. 1283-1287
Author(s):  
Wen Tzeng Huang ◽  
Hung Li Tseng ◽  
Jian Cheng Dai ◽  
Chin Hsing Chen ◽  
Sun Yen Tan
Keyword(s):  
Image Processing ◽  
High Speed ◽  
High Reliability ◽  
Processing System ◽  
Simulation Software ◽  
Image Process ◽  
Image Processing System ◽  
Signal Line ◽  
Process System ◽  
Standard Value

With continuous advancement in science and technology, the image quality has entered an era of full-HD. This study developed a high-reliability image processing system platform, based on the FPGA platform. By using a high-reliability hardware platform development process, and with the aid of the simulation software, this study simulated the transmission integrity of the high-speed digital signals on the PCB. The proposed method was used to build a FPGA-based high-reliability image processing system platform. The implementation in this study, with the length of the Clock and DQS signal line of DDR2 being controlled within 555 mil, was discussed, and the errors were analyzed. The simulated value of the tDQSCK was 195.048 ps, the measured value was 215 ps, and the standard value of the JEDEC was less than 350 ps. Between the simulated value and the measured value, there was only an error of about 9.3%, which meets the reliability requirement. The length tolerance of the signal line laid was 38.5% better than the standard value of the JEDEC.

scholarly journals Identification of Feasibility of Canned Based Foods Image Processing Techniques Using Thresholding

2021 ◽  
Vol 3 (1) ◽  
pp. 10
Author(s):  
Ilhamsyah Muhammad Nurdin ◽  
Abdul Fadlil
Keyword(s):  
Image Processing ◽  
Visual Observation ◽  
Android Application ◽  
File Transfer Protocol ◽  
File Transfer ◽  
Canned Food ◽  
Image Processing Techniques ◽  
Accuracy Level ◽  
Processing Techniques

Eye sight is sometimes deceptive, especially in determining the quality of a canned food, so it is necessary to use technology that resembles human visual observation, namely in the form of an application. The process to detect the quality of canned food uses image processing methods, especially thresholding, which is then designed so that the application is able to determine the quality of canned food with the help of the MATLAB GUI which detects and then sends it from making the MATLAB GUI on the Laptop to Android using FTP (File Transfer Protocol). At the end of the process, it is marked with known good and bad quality of canned food with an android application that has been specially designed with an accuracy level of 84% with a thresholding value of 70.

scholarly journals Detection and Contouring of BAU-Kul using Image Processing Techniques

2019 ◽  
Vol 23 (2) ◽  
pp. 15-25
Author(s):  
MM Rahman ◽  
MMH Oliver
Keyword(s):  
Image Processing ◽  
High Accuracy ◽  
Contour Detection ◽  
Average Error ◽  
Market Prices ◽  
Image Processing Techniques ◽  
Random Samples ◽  
Automated Grading ◽  
Processing Techniques ◽  
Locally Grown

Automated grading and sorting of fruits during harvesting period are needed for securing better market prices. In order to introduce such automation facilities in Bangladesh, edging and contouring information of the locally grown fruits is important. This study reports the first endeavor towards the use of image processing techniques for a popular jujube variety (BAU-Kul) in Bangladesh. Image processing techniques were used for segmentation, and contouring on the basis of color Thresholding, edge detection and contour detection in Python-OpenCV software. Six random samples of BAU-Kul fruit were used for the research. Perimeter lengths obtained from the image analysis of the six samples ranged from 17.9 cm to 20.20 cm with an average of 19.29 (±1.02) cm. The measured lengths on the other hand, varied from 16.2 cm to 19.1 cm with an average of 17.75 (±1.3) cm. Consequently, the average error in calculation was limited to only 7.98%. This indicates the fact that images captured through mobile devices can be used for detection and contouring of BAU-Kul samples with fairly high accuracy (92.02%). These information provides a foreground basis of automation for the grading and sorting systems of BAU-Kul fruits in Bangladesh. Ann. Bangladesh Agric. (2019) 23(2) : 15-25

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>

scholarly journals Implementation and Optimization of Image Processing on the Map of SABRE i.MX_6

2021 ◽  
pp. 402-417
Author(s):  
Waheed Muhammad SANYA ◽  
Gaurav BAJPAI ◽  
Haji Ali HAJI
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Median Filter ◽  
Random Noise ◽  
Optimization Techniques ◽  
Image Process ◽  
Algorithm Optimization ◽  
Visual Appearance ◽  
Algorithmic Optimization ◽  
Different Response

Vision relieves humans to understand the environmental deviations over a period. These deviations are seen by capturing the images. The digital image plays a dynamic role in everyday life. One of the processes of optimizing the details of an image whilst removing the random noise is image denoising. It is a well-explored research topic in the field of image processing. In the past, the progress made in image denoising has advanced from the improved modeling of digital images. Hence, the major challenges of the image process denoising algorithm is to advance the visual appearance whilst preserving the other details of the real image. Significant research today focuses on wavelet-based denoising methods. This research paper presents a new approach to understand the Sobel imaging process algorithm on the Linux platform and develop an effective algorithm by using different optimization techniques on SABRE i.MX_6. Our work concentrated more on the image process algorithm optimization. By using the OpenCV environment, this paper is intended to simulate a Salt and Pepper noisy phenomenon and remove the noisy pixels by using Median Filter Algorithm. The Sobel convolution method included and used in the design of a Sobel Filter and then process the image following the median filter, to achieve an effective edge detection result. Finally, this paper optimizes the algorithm on SABRE i.MX_6 Linux environment. By using algorithmic optimization (lower complexity algorithm in the mathematical sense, using appropriate data structures), optimization for RISC (loop unrolling) processors, including optimization for efficient use of hardware resources (access to data, cache management and multi-thread), this paper analyzed the different response parameters of the system with varied inputs, different compiler options (O1, O2, or O3), and different doping degrees. The proposed denoising algorithm shows the meaningful addition of the visual quality of the images and the algorithmic optimization assessment.

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