Work place: Department of Computer Science and Engineering Krishna Institute of Engineering and Technology Ghaziabad, India
E-mail: Anandskla@gmail.com
Website:
Research Interests: Pattern Recognition, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms, Cellular Automata
Biography
Anand Prakash Shukla is born in year 1979. Obtained B.E Degree in Computer Science and Engineering in 2000 and M.Tech in Computer Science and Engineering in 2005 and currently pursuing Ph.D from Computer Science and Engineering department, Motilal Nehru National Institute of Technology, Allahabad and working as Associate Professor in Department of Computer Science and Engineering, Krishna Institute of Engineering and Technology (KIET), Ghaziabad, India. His current research interest includes cellular automata, Image processing and pattern recognition.
By Nidhi Gupta A.P Shukla Suneeta Agarwal
DOI: https://doi.org/10.5815/ijieeb.2016.03.02, Pub. Date: 8 May 2016
Acquisition of digital image and preprocessing methods plays a vital role in clinical diagnosis. The ultrasound medical images are more popular than other imaging modalities, due to portable, adequate, harmless and cheaper nature of it. Because of intrinsic nature of speckle noise (signal based noise), ultrasound medical image leads to degradation of the resolution and contrast of the image. Reduction of this signal based noise is helpful for the purpose of visualization of the ultrasound images. The low quality of image is considered as a barrier for the better extraction of features, recognition, analysis and detection of edges. Because of which inappropriate diagnosis may be done by doctor. Thus, speckle noise reduction is essential and preprocessing step of ultrasound images. Analysts survey manifold reduction methods of speckle noise, yet there is no exact method that takes all the limitations into account. In this review paper, we compare filters that are Lee, Frost, Median, SRAD, PMAD, SRBF, Bilateral, Adaptive Bilateral and Multiresolution on medical ultrasound images. The results are compared with parameter PSNR along with the visual inspection. The conclusion is illustrated by filtered images and data tables.
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