scholarly journals Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Vahid Faghih Dinevari ◽  
Ghader Karimian Khosroshahi ◽  
Mina Zolfy Lighvan
Keyword(s):  
Singular Value Decomposition ◽  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
Singular Value ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Data Set ◽  
Value Decomposition ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Dong Liu ◽  
Xu Lai ◽  
Zhihuai Xiao ◽  
Dong Liu ◽  
Xiao Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Singular Value Decomposition ◽  
Convolutional Neural Network ◽  
Rotating Machinery ◽  
Singular Value ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Comparison Results ◽  
Value Decomposition

Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods.

scholarly journals Organic Boundary Location Based on Color-Texture of Visual Perception in Wireless Capsule Endoscopy Video

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengliang Wang ◽  
Zhuo Luo ◽  
Xiaoqi Liu ◽  
Jianying Bai ◽  
Guobin Liao
Keyword(s):  
Visual Perception ◽  
Capsule Endoscopy ◽  
Color Space ◽  
Wireless Capsule Endoscopy ◽  
Support Vector ◽  
Svm Classifier ◽  
Phase Congruency ◽  
Region Detection ◽  
Wireless Capsule ◽  
Color Texture

This paper addresses the problem of automatically locating the boundary between the stomach and the small intestine (the pylorus) in wireless capsule endoscopy (WCE) video. For efficient image segmentation, the color-saliency region detection (CSD) method is developed for obtaining the potentially valid region of the frame (VROF). To improve the accuracy of locating the pylorus, we design the Monitor-Judge model. On the one hand, the color-texture fusion feature of visual perception (CTVP) is constructed by grey level cooccurrence matrix (GLCM) feature from the maximum moments of the phase congruency covariance and hue-saturation histogram feature in HSI color space. On the other hand, support vector machine (SVM) classifier with the CTVP feature is utilized to locate the pylorus. The experimental results on 30 real WCE videos demonstrate that the proposed location method outperforms the related valuable techniques.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

scholarly journals Implementasi Teknik Watermarking Menggunakan Metode Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD) pada Citra Digital

2021 ◽  
Vol 14 (2) ◽  
pp. 125
Author(s):  
Verryna Adzillatul Fathiha
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Value Decomposition

Watermarking merupakan teknik penyembunyian data/informasi kedalam suatu citra digital yang tidak kasat mata atau tidak dapat diketahui secara visual. Penyembunyian data/informasi kedalam citra tersebut bersifat rahasia. Karena tahan terhadap proses digitalisasi, teknik watermarking dapat digunakan untuk melindungi kepemilikan suatu citra digital. Ada tiga kriteria yang harus diperhatikan dalam watermarking pada citra digital, diantaranya adalah security, impreceptibily, dan robustness. Pada tugas Penulisan Karya Ilmiah (PI) ini dibuat suatu program watermarking menggunakan metode Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD) yang kemudian juga akan dipaparkan bagaimana cara menggunakan program watermarking yang telah dibuat. Keunggulan dari program ini citra yang digunakan dalam program watermarking ini dapat berupa citra berwarna maupun citra grayscale. Program watermarking ini dibuat menggunakan bahasa pemrograman C++ melalui aplikasi Matlab Kata Kunci            : Wateramarking , Singular Value Decomposition (SVD), Discrete Wavelet Transform (DWT), Matlab, Bahasa Pemrograman C++.

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