An Intelligence System to Detect and Analyze Disease in Ultrasound Images

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-22 Number-1
Year of Publication : 2015
Authors : Yahya Shuaibu, Rajiv Kumar, Surayya Ado


Yahya Shuaibu, Rajiv Kumar, Surayya Ado "An Intelligence System to Detect and Analyze Disease in Ultrasound Images". International Journal of Computer Trends and Technology (IJCTT) V22(1):26-29, April 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
With the recent advances in the field of computer science and technology and the introduction of image processing as a field of computer science and information technology, enhancement in the interpretation of the medical images has contributed to the early diagnosis of various diseases in human beings. This paper provide the design and implementation of an intelligent system that would be used to detect disease form an ultra sound images with a specification and limit in kidney stone, although the scope of the project can be further extends and re-used in detecting several other diseases related to medical imaging. The system will be implemented using Holomorphic filter for smoothing the ultrasound images. The Speckle Reduction Anisotropic Diffusion (SARD) is used as a techniques for reducing a speckle noise which is very common in medical images, SRAD is the edge-sensitive diffusion for speckled images, in the same way that conventional anisotropic diffusion is the edge-sensitive diffusion for images corrupted with additive noise. A technique of de blurring a Gaussian blur will be used for smoothing the images as a post processing steps after filtering to increase efficiency and smoothness of the image. The Geodesic Active Contour method is used for segmenting an image and the defected image can be converted to RGB Image to enhance image and increase its readability to enable the medical practitioners to interprets the images and relate it’s to its various considerations.

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Ultrasound image, Noise Removal, Segmentation, Disease Detection and Analysis.