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Automatic region of interest generation for kidney ultrasound images

Wan, Mahani Hafizah and Supriyanto, Eko (2011) Automatic region of interest generation for kidney ultrasound images. In: Recent Researches in Applied Informatics and Remote Sensing, 29-31 December 2011, Montreux, Switzerland.

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Abstract

Ultrasound scanning of the kidney is performed to assess kidney size, shape and location as well as to detect any abnormalities in kidney like cysts and stones. Since ultrasound image contains speckle noise, performing the segmentation methods for the kidney images has always been a very challenging task. For further segmentation purpose, deleting and removing the complicated background not only speeds up the segmentation process, but also increases accuracy. However, in previous studies, the ROI of the kidney is manually cropped. Therefore, this study proposed an automatic region of interest (ROI) generation for kidney ultrasound images. The methods consist of the speckle noise reduction using Gaussian low-pass filter, texture analysis by calculating the local entropy of the image, threshold selection, morphological operations, object windowing, determination of seed point and last but not least the ROI generation. This algorithm has been tested to more than 200 kidney ultrasound images. As the result, for longitudinal kidney images, out of 120 images, 109 images generate true ROI (91%) and another 11 images generate false ROI (9%). For transverse kidney images, out of 100 images, 89 images generate true ROI (89%) and 11 images generate false ROI (11%). To conclude, the method in this study can be practically used for automatic generation of US kidney ROI.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: kidney ultrasound; region of interest; seed point; texture analysis; morphological operation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electronic Engineering
Depositing User: Dr. Wan Mahani Hafizah Wan Mahmud
Date Deposited: 24 Jul 2014 06:13
Last Modified: 24 Jul 2014 06:13
URI: http://eprints.uthm.edu.my/id/eprint/5708
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