Detection of Prostate Cancer from RF Ultrasound Echo Signals Using Fractal Analysis

TitleDetection of Prostate Cancer from RF Ultrasound Echo Signals Using Fractal Analysis
Publication TypeConference Paper
Year of Publication2006
AuthorsMoradi, M., P. Abolmaesumi, P. A. Isotalo, D. R. Siemens, E. E. Sauerbrei, and P. Mousavi
Conference NameEngineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Pagination2400 -2403
Date Publishedaug.
KeywordsAHDRFT, average Higuchi fractal dimensions, B-scan images, biological organs, biomedical ultrasonics, cancer, feature extraction, fractal analysis, fractals, histopathologic analysis, image classification, image statistical moments, image texture, neural nets, neural-network-based classification procedure, prostate cancer detection, prostate tissue, recorded backscattered echoes, RF data collection, RF time series, RF ultrasound echo signals, statistical analysis, texture feature extraction, time series, tumours, ultrasound data

In this paper we propose a new feature, average Higuchi dimension of RF time series (AHDRFT), for detection of prostate cancer using ultrasound data. The proposed feature is extracted from RF echo signals acquired from prostate tissue in an in vitro setting and is used in combination with texture features extracted from the corresponding B-scan images. In a novel approach towards RF data collection, we continuously recorded backscattered echoes from the prostate tissue to acquire time series of the RF signals. We also collected B-scan images and performed a detailed histopathologic analysis on the tissue. To compute AHDRFT, the Higuchi fractal dimensions of the RF time series were averaged over a region of interest. AHDRFT and texture features extracted from corresponding B-scan images were used to classify regions of interest, as small as 0.028 cm of the prostate tissue in cancerous and normal classes. We validated the results based on our histopathologic maps. A combination of image statistical moments and features extracted from co-occurrence matrices of the B-scan images resulted in classification accuracy of around 87%. When AHDRFT was added to the feature vectors, the classification accuracy was consistently over 95% with best results of over 99% accuracy. Our results show that the RF time series backscattered from prostate tissues contain information that can be used for detection of prostate cancer


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