scholarly journals A Methodological Approach for Early Melanoma Detection Using Smartphone Captured Images

2019 ◽  
pp. 503-510
Author(s):  
Haritha U ◽  
Muhammad Shameem
Keyword(s):  
Malignant Melanoma ◽  
Methodological Approach ◽  
Selection Criterion ◽  
Image Features ◽  
Motion Blur ◽  
Color Variation ◽  
Correct Result ◽  
Melanoma Detection ◽  
Blurred Image ◽  
New Feature

Researches on applications of mobile devices bring wide variety of uses in healthcare. One such work focus on detection of malignant melanoma using mobile image analysis. Dermoscopy is one of a current use, but need a special expertise for the detection of cancer melanoma. The image taken using smartphone is used for this purpose. It mainly focus on localization of the skin lesion by combining fast skin detection and fusion of two fast segmentation results. This also introduces some set of image features and to capture color variation and border irregularity which are useful for smartphone-captured images. It propose a new feature selection criterion to select a small set of good features used in the final lightweight system. The method introduces a new module for the detection of distorted images such as motion blur and alert users in such situations. The blurred image undergo deblurring to detect the correct result. The result of this application will identify whether the image is malignant melanoma or benign with their intensity value from smartphone captured images used.

scholarly journals A multi-tree genetic programming representation for melanoma detection using local and global features

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Harith Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Skin Cancer ◽  
Genetic Programming ◽  
Early Stage ◽  
Extraction Methods ◽  
Image Features ◽  
Color Variation ◽  
Genetic Operators ◽  
Global Features ◽  
Melanoma Detection ◽  
Performance Gains

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.

Parameter Estimation and Restoration of Motion Blurred Image

2014 ◽  
Vol 608-609 ◽  
pp. 855-859 ◽  
Author(s):  
Yu Xiang Song ◽  
Yan Mei Zhang
Keyword(s):  
Parameter Estimation ◽  
Image Restoration ◽  
Rotation Axis ◽  
Image Features ◽  
Motion Blur ◽  
Binary Image ◽  
Degraded Image ◽  
Image Center ◽  
Blurred Image ◽  
The Difference

according to the real motion blur image restoration problems, analyze the difference between the image features and Simulation of real blurred images, this paper proposes a method that applied to real image degradation parameter estimation. First calculate the degraded image using cepstrum, taking the cepstrum to binary image using absolute value of minimum gray as the threshold, and then remove the center cross bright line; and then use formula of point to line to calculate the distance of bright fringe direction of binary image, that is direction of motion blur; the direction of motion blur is rotated to the horizontal direction by the degraded image center of rotation axis, divided the autocorrelation method to calculate fuzzy scale. To estimate the point spread function is take into the Wiener filtering algorithm to recover images, image restoration effect prove that parameter estimation results are correct.

scholarly journals A multi-tree genetic programming representation for melanoma detection using local and global features

2020 ◽  
Author(s):  
Q Ul Ain ◽  
Harith Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Skin Cancer ◽  
Genetic Programming ◽  
Early Stage ◽  
Extraction Methods ◽  
Image Features ◽  
Color Variation ◽  
Genetic Operators ◽  
Global Features ◽  
Melanoma Detection ◽  
Performance Gains

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer.

scholarly journals SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3641
Author(s):  
Hui Feng ◽  
Jundong Guo ◽  
Haixiang Xu ◽  
Shuzhi Sam Ge
Keyword(s):  
Receptive Field ◽  
Evaluation Criteria ◽  
Objective Evaluation ◽  
Detection Algorithm ◽  
Image Features ◽  
Motion Blur ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Blurred Image

Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network’s ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.

scholarly journals hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

2021 ◽  
Author(s):  
Sümeyya İlkin ◽  
Tuğrul Hakan Gençtürk ◽  
Fidan Kaya Gülağız ◽  
Hikmetcan Özcan ◽  
Mehmet Ali Altuncu ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Optimization Algorithm ◽  
Bacterial Colony ◽  
Melanoma Detection

scholarly journals Evaluation and Validation of a Semiautomatic Image Analysis Method: Quantification of Nares Cell Proliferation of Rhinella arenarum (Anura) Detected with the Avidin-Biotin Complex/Peroxidase, DAB

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Javier Eduardo Diaz Zamboni ◽  
Daniela Osella ◽  
Enrique Valentín Paravani ◽  
Víctor Hugo Casco
Keyword(s):  
Gold Standard ◽  
Visual Analysis ◽  
Methodological Approach ◽  
Image Features ◽  
Analysis Method ◽  
New Approach ◽  
Image Characteristics ◽  
Microscopy Image ◽  
Computer Based ◽  
Current Report

The current report presents the development and application of a novel methodological approach for computer-based methods of processing and analysis of proliferative tissues labeled by ABC-peroxidase method using 3, 3′-diaminobenzidine (DAB) as chromogen. This semiautomatic method is proposed to replace the classical manual approach, widely accepted as gold standard. Our method is based on a visual analysis of the microscopy image features from which a computational model is built to generate synthetic images which are used to evaluate and validate the methods of image processing and analysis. The evaluation allows knowing whether the computational methods applied are affected by the change of the image characteristics. Validation allows determining the method’s reliability and analyzing the concordance between the proposed method and a gold standard one. Additional strongness of this new approach is that it may be a framework adaptable to other studies made on any kind of microscopy.

scholarly journals Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Eunsung Lee ◽  
Eunjung Chae ◽  
Hejin Cheong ◽  
Joonki Paik
Keyword(s):  
Inertial Sensors ◽  
Motion Blur ◽  
Trajectory Estimation ◽  
Motion Trajectories ◽  
Motion Deblurring ◽  
Spread Function ◽  
Blurred Image ◽  
Fast Motion ◽  
Noise Amplification ◽  
Spatially Varying

This paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. The proposed method estimates a point-spread-function (PSF) of motion blur by accumulating reweighted projections of the trajectory. A motion blurred image is then adaptively restored using the estimated PSF and spatially varying activity map to reduce both restoration artifacts and noise amplification. Experimental results demonstrate that the proposed method outperforms existing PSF estimation-based motion deconvolution methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed in various imaging devices because of its efficient implementation without an iterative computational structure.

Human malignant melanoma: detection of BRAF- and c-kit–activating mutations by high-resolution amplicon melting analysis

2005 ◽  
Vol 36 (5) ◽  
pp. 486-493 ◽  
Author(s):  
Carlynn Willmore-Payne ◽  
Joseph A. Holden ◽  
Sheryl Tripp ◽  
Lester J. Layfield
Keyword(s):  
High Resolution ◽  
Malignant Melanoma ◽  
Human Malignant Melanoma ◽  
Activating Mutations ◽  
Melanoma Detection ◽  
Melting Analysis
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