Abnormality Detection in Kidney Ultrasound Images by Various Classifiers with FPGA

Abstract

Ultrasound scanning has been used as the preliminary diagnosis tool all over the world. The ultrasound data are being analyzed by tele-radiologists. It lacks online availability. The drawbacks of tele-radiology have been overcome by using computer-aided diagnosis. As an aid to this, the Random Forest Classifier has been used here for detecting kidney abnormalities. The initial pre-processing stage filters the speckle noise existing in the input kidney image. Then feature extraction has been performed. Image categorization as normal, cyst and stone has been done with Random Forest Classifier. Then the performance is evaluated by comparing it with K-nearest neighbor classifier and support vector machine. From experimentation, it is observed that the accuracy and F-measure values of Random Forest Classifier range high when compared with other classifiers due to the continuous split of the trees until accurate categorization is done. Simulation and Implementation have been done using Modelsim 6.4a and Xilinx Spartan-6 FPGA board.

Keywords:

• Block RAM
• Computer-aided design
• Field programmable gate array
• K-nearest neighbor
• Random forest classifier
• Speckle

ACKNOWLEDGEMENTS

The authors would like to thank the Principal and Department of ECE, Mepco Schlenk Engineering College, Sivakasi for providing all the facilities to carry out this work successfully.

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

B. Vijayakumari

B Vijayakumari completed BE at Institute of Road and Transport Technologies, Erode, Tamilnadu during the year 1997 and received ME (Communication Systems) from Thiagarajar College of Engineering, Madurai, Tamilnadu during the year 2006. She has completed PhD in medical image processing at Thiagarajar College of Engineering, Madurai during the year 2014 and is currently working as an associate professor in the Department of ECE at Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India. Her research area includes medical image processing and image analysis. To her credit, she had published 21 papers in peer reviewed and science citation indexed journals both national and international and 40 papers in conferences. She is a Life Member of Indian Society for Technical Education (ISTE) and The Institution of Electronics and Telecommunication Engineers (IETE). Corresponding author. Email: [email protected]

S. Rashmita

S Rashmita is a PG student studying ME (VLSI Design) at Mepco Schlenk Engineering College, Sivakasi, Tamilnadu. She had completed her UG in electronics and communication engineering during the year 2017 at Ponjesly Engineering College, Nagercoil, Tamilnadu. Her research area includes development of image processing algorithms on FPGA platforms. Email: [email protected]