Ultrasound Kidney Image Analysis for Computerized Disorder Identification and Classification Using Content Descriptive Power Spectral Features

Abstract

The objective of this work is to classify few important kidney categories by characterizing the tissues of kidney region using the unique power spectral features with ultrasound as imaging modality. The images are acquired from male and female subjects of age 45 ± 15 years. Three kidney categories namely normal, medical renal diseases and cortical cyst are considered for the analysis. The acquired images are initially pre-processed to retain the pixels-of-interest. The proposed features depend on the spatial distribution of spectral components in the kidney region. A set of power spectral features ${\rm P}^{{W_{1} }}_{T} ,\,{\rm P}^{{W_{2} }}_{T} ,\,{\rm P}^{{R_{1} }}_{{T - W_{{12}} }} ,\,{\rm P}^{{R_{2} }}_{{T - W_{{12}} }} ,\,{\rm P}^{{R_{3} }}_{{T - W_{{1d}} }}$ and ${\rm P}^{{R_{4} }}_{{T - W_{{1d}} }}$ are estimated at the specific cut-off frequencies Ω _rc1 and Ω _rc2 in the spectrum and by considering global mean total power. The results obtained show that the features are highly content descriptive and provide discrete range of values for each kidney category. Such isolated feature values facilitate to identify the kidney categories objectively which may be used as a secondary observer. The proposed method and features also explores the possibility of implementing computer-aided diagnosis system exclusively for US kidney images.