scholarly journals Automatic Liver Cancer Detection using Sobel Edge Detection & Morphological Dilation in Digital Image Processing

2020 ◽  
Vol 9 (12) ◽  
pp. 364-378
Keyword(s):  
Image Processing ◽  
Liver Cancer ◽  
Error Rate ◽  
Medical Science ◽  
Turing Test ◽  
Sensitive Information ◽  
Rate System ◽  
Sobel Operator ◽  
Filtering Technique ◽  
Sobel Edge Detection

Image processing is a field that is widely used in medical science to identify various cancers or tumors. Diagnosing liver cancer is not an easy task and is usually performed by doctors and diagnosed manually. Filtering technique should be used precisely by not compromising the sensitive information. Most of the technique may distort the actual information that causes false alarm rate. A liver is an uneven or bit complex in structure where there are various spots may be considered as tumor that provokes the system towards invalid turing test. This paper proposes a system that would be able to recognize cancer automatically from a tomographical image along with high precision that stabilize the system with less processing time. Here the objective of the system is to obtain the result using Sobel operator that retains edges and eroding the unwanted areas and preceding high accuracy with less error rate. System also intended to extract the impaired area that has been affected by liver cancer. System acquired the better precision rate as compare to the previously implemented systems with minimal error rate.

scholarly journals More human than human: a Turing test for photographed faces

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Jet Gabrielle Sanders ◽  
Yoshiyuki Ueda ◽  
Sakiko Yoshikawa ◽  
Rob Jenkins
Keyword(s):  
Social Context ◽  
Personal Identity ◽  
Error Rate ◽  
Turing Test ◽  
Perceptual Task ◽  
Viewing Time ◽  
Facial Appearance ◽  
Theoretical Interest ◽  
Significant Performance ◽  
Recent Experimental Work

Abstract Background Recent experimental work has shown that hyper-realistic face masks can pass for real faces during live viewing. However, live viewing embeds the perceptual task (mask detection) in a powerful social context that may influence respondents’ behaviour. To remove this social context, we assessed viewers’ ability to distinguish photos of hyper-realistic masks from photos of real faces in a computerised two-alternative forced choice (2AFC) procedure. Results In experiment 1 (N = 120), we observed an error rate of 33% when viewing time was restricted to 500 ms. In experiment 2 (N = 120), we observed an error rate of 20% when viewing time was unlimited. In both experiments we saw a significant performance cost for other-race comparisons relative to own-race comparisons. Conclusions We conclude that viewers could not reliably distinguish hyper-realistic face masks from real faces in photographic presentations. As well as its theoretical interest, failure to detect synthetic faces has important implications for security and crime prevention, which often rely on facial appearance and personal identity being related.

Image Processing Based on Three-Dimensional Stereo Vision

2014 ◽  
Vol 556-562 ◽  
pp. 5017-5020
Author(s):  
Ting Ting Wang
Keyword(s):  
Image Processing ◽  
Binocular Vision ◽  
Stereo Vision ◽  
Error Rate ◽  
Three Dimensional ◽  
Processing Method ◽  
Stereoscopic Vision ◽  
Average Error ◽  
Traditional Model ◽  
Average Error Rate

Three-dimensional stereo vision technology has the capability of overcoming drawbacks influencing by light, posture and occluder. A novel image processing method is proposed based on three-dimensional stereoscopic vision, which optimizes model on the basis of camera binocular vision and in improvement of adding constraints to traditional model, moreover ensures accuracy of later location and recognition. To verify validity of the proposed method, firstly marking experiments are conducted to achieve fruit location, with the result of average error rate of 0.65%; and then centroid feature experiments are achieved with error from 5.77mm to 68.15mm and reference error rate from 1.44% to 5.68%, average error rate of 3.76% while the distance changes from 300mm to 1200mm. All these data of experiments demonstrate that proposed method meets the requirements of three-dimensional imageprocessing.

Detection of Microbubbles Using the Hough Transform

2013 ◽  
Vol 378 ◽  
pp. 478-482
Author(s):  
Yoshihiro Mitani ◽  
Toshitaka Oki
Keyword(s):  
Image Processing ◽  
Error Rate ◽  
Hough Transform ◽  
Detection Method ◽  
Experimental Results ◽  
Average Error ◽  
Image Processing Techniques ◽  
Average Error Rate ◽  
Processing Techniques

The microbubble has been widely used and shown to be effective in various fields. Therefore, there is an importance of measuring accurately its size by image processing techniques. In this paper, we propose a detection method of microbubbles by the approach based on the Hough transform. Experimental results show only 4.49% of the average error rate of the undetected microbubbles and incorrectly detected ones. This low percentage of the error rate shows the effectiveness of the proposed method.

scholarly journals UAV IMAGE BLUR – ITS INFLUENCE AND WAYS TO CORRECT IT

2015 ◽  
Vol XL-1/W4 ◽  
pp. 33-39 ◽  
Author(s):  
T. Sieberth ◽  
R. Wackrow ◽  
J. H. Chandler
Keyword(s):  
Image Processing ◽  
Target Identification ◽  
Image Sequences ◽  
Identification Measurements ◽  
Filtering Technique ◽  
Coordinate Measurements ◽  
Active Research ◽  
Uav Image ◽  
Blurred Images ◽  
Degradation Effect

Unmanned aerial vehicles (UAVs) have become an interesting and active research topic in photogrammetry. Current research is based on image sequences acquired by UAVs which have a high ground resolution and good spectral resolution due to low flight altitudes combined with a high-resolution camera. One of the main problems preventing full automation of data processing of UAV imagery is the unknown degradation effect of blur caused by camera movement during image acquisition. <br><br> The purpose of this paper is to analyse the influence of blur on photogrammetric image processing, the correction of blur and finally, the use of corrected images for coordinate measurements. It was found that blur influences image processing significantly and even prevents automatic photogrammetric analysis, hence the desire to exclude blurred images from the sequence using a novel filtering technique. If necessary, essential blurred images can be restored using information of overlapping images of the sequence or a blur kernel with the developed edge shifting technique. The corrected images can be then used for target identification, measurements and automated photogrammetric processing.

Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

Study of Noise Removal Techniques for Digital Images

Biometrics ◽  
2017 ◽  
pp. 1105-1144
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Study of Noise Removal Techniques for Digital Images

2016 ◽  
pp. 48-87
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Bio-Medical Image Processing

2020 ◽  
pp. 158-169
Author(s):  
Rasmita Lenka ◽  
Koustav Dutta ◽  
Ashimananda Khandual ◽  
Soumya Ranjan Nayak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Early Detection ◽  
Digital Image Processing ◽  
Medical Science ◽  
Medical Image Processing ◽  
The World ◽  
Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

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