An image analysis method for prostate tissue classification: preliminary validation with resonance sensor data

Resonance sensor systems have been shown to be able to distinguish between cancerous and normal prostate tissue, in vitro. The aim of this study was to improve the accuracy of the tissue determination, to simplify the tissue classification process with computerized morphometrical analysis, to decrea...

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Bibliographic Details
Published in:Journal of medical engineering & technology 2009, Vol.33 (1), p.18-24
Main Authors: Lindberg, P. L., Andersson, B. M., Bergh, A., Ljungberg, B., Lindahl, O. A.
Format: Article
Language:English
Subjects:
Algorithms
classification
Diagnostic Imaging - methods
Equipment Design
fysik
Humans
Image analysis
Image Processing, Computer-Assisted - instrumentation
Image Processing, Computer-Assisted - methods
Least-Squares Analysis
Linear Models
Male
Medical engineering
Medical Engineering for Healthcare
Medicinsk teknik
Medicinsk teknik för hälsovård
Multivariate Analysis
Other technology
Partial least square
Physics
Prostate - pathology
Prostate cancer
prostate tissue
Prostatic Neoplasms - diagnosis
Reproducibility of Results
Resonance sensor
Signal Processing, Computer-Assisted
Stiffness
TECHNOLOGY
TEKNIKVETENSKAP
Övriga teknikvetenskaper
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Summary:Resonance sensor systems have been shown to be able to distinguish between cancerous and normal prostate tissue, in vitro. The aim of this study was to improve the accuracy of the tissue determination, to simplify the tissue classification process with computerized morphometrical analysis, to decrease the risk of human errors, and to reduce the processing time. In this article we present our newly developed computerized classification method based on image analysis. In relation to earlier resonance sensor studies we increased the number of normal prostate tissue classes into stroma, epithelial tissue, lumen and stones. The linearity between the impression depth and tissue classes was calculated using multiple linear regression (R2 = 0.68, n = 109, p < 0.001) and partial least squares (R2 = 0.55, n = 109, p < 0.001). Thus it can be concluded that there existed a linear relationship between the impression depth and the tissue classes. The new image analysis method was easy to handle and decreased the classification time by 80%.
ISSN:0309-1902
1464-522X
1464-522X
DOI:10.1080/03091900801945200