Texture-driven super-resolution of ultrasound images using optimized deep learning model

ABSTRACT

While comparing to the additional medical imaging modalities, the low resolution (LR) with poor quality images are obtained because of natural intrinsic imaging characteristics. We proposed a novel deep learning- based super-resolution of ultrasound images that are texture-driven. In this study, Convolutional Neural Networks (CNNs) are used to speed up the process to increase the image quality. Additionally, the Dwarf Mongoose Optimization (DMO) method is used to adjust the parameters of CNN thereby improving the quality of the image resolutions. The textures of the super-resolution images are examined with the Histogram of Oriented Gradients (HOG). The experimental works are handled using python software. Super-resolution per second and PSNR/dB values for the proposed model are 0.289 and 42.340, respectively. This model also offers FSO, GMSD, MAD, and MSSIM values of 0.9712, 0.013, 0.9802, and 0.9832 which is relatively higher than the existing techniques.

KEYWORDS:

• Super resolution
• ultra sound imaging
• Convolutional Neural Network
• Histogram of Oriented Gradients
• low resolution
• texture
• CAD diagnosis
• deep learning
• dwarf mongoose optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data and material

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

M. Markco

M. Markco obtained his bachelor's degree in Computer Science and Engineering from E.G.S Pillay Engineering College Nagapattinam in the year 2009 and obtained Master's Degree in the year 2011from Anna University, Coimbatore, India. He is currently pursuing Ph.D., in Anna University, Chennai, India and working as an Assistant Professor in the Faculty of Computer Science and Engineering at E.G.S Pillay Engineering College Nagapattinam, India. His area of specialization includes Wireless Network, Image Processing and Data mining. His current research interest is Image processing. He is a member of ISTE and IE.

S. Kannan

Dr. S. Kannan is a Professor of Electrical and Electronics Communication Engineering, Kings College of Engineering, Pudukottai. He holds MS in Information Technology from Bharathidhasan University at Tiruchirappalli, ME in Software Engineering at Periyar Maniammai College of Technology for women, Anna University Chennai and PhD in Computer Science and Engineering from Manonmaniam Sundaranar University at Tirunelveli. He special interests include Business Intelligence Systems, Data Mining, Wireless Sensor Network and soft computing.