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Investigation of kidney ultrasound image texture features for healthy subject

Wan Kairuddin, Wan Nur Hafsha (2016) Investigation of kidney ultrasound image texture features for healthy subject. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Image feature extraction is a technique to identify the characteristic of the image. In this thesis, the focus is on a healthy kidney ultrasound image. The main objective is to select the features that best describe a tissue characteristic of a healthy kidney from ultrasound image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of ultrasound image. Initially, the acquired images are manually cropped to get the region of interest (ROI) of kidney. Then the cropped images are undergoing filtering process to remove the speckle noise that presence in the ultrasound images. Four filtering techniques (Wiener filter, Median filter, Gaussian Low Pass filter and Histogram Equalization) are tested to find the best filtering technique for all the three groups of image. By calculating Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR), result shows that Gaussian Lowpass Filter having the highest PSNR, then is choose as the filtering method for this thesis. These enhanced images then are segment to create a foreground and background where the mask is created. In this thesis, only statistical based texture features method is used which depends on the spatial distribution of intensity values or gray levels in the kidney region. Three statistical feature extractions techniques used are Intensity Histogram (IH), Gray- Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM). By using One-Way ANOVA in SPSS, the result shows that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe healthy kidney characteristics regardless of the ultrasound image quality.

Item Type: Thesis (Masters)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 09 May 2017 07:34
Last Modified: 09 May 2017 07:34
URI: http://eprints.uthm.edu.my/id/eprint/9103
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