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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| Veröffentlicht in: | Journal of medical engineering & technology 2009, Vol.33 (1), p.18-24 |
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| creator | Lindberg, P. L. Andersson, B. M. Bergh, A. Ljungberg, B. Lindahl, O. A. |
| description | 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%. |
| doi_str_mv | 10.1080/03091900801945200 |
| format | Article |
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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. 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L.</creatorcontrib><creatorcontrib>Andersson, B. M.</creatorcontrib><creatorcontrib>Bergh, A.</creatorcontrib><creatorcontrib>Ljungberg, B.</creatorcontrib><creatorcontrib>Lindahl, O. A.</creatorcontrib><title>An image analysis method for prostate tissue classification: preliminary validation with resonance sensor data</title><title>Journal of medical engineering & technology</title><addtitle>J Med Eng Technol</addtitle><description>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. 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The new image analysis method was easy to handle and decreased the classification time by 80%.</description><subject>Algorithms</subject><subject>classification</subject><subject>Diagnostic Imaging - methods</subject><subject>Equipment Design</subject><subject>fysik</subject><subject>Humans</subject><subject>Image analysis</subject><subject>Image Processing, Computer-Assisted - instrumentation</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Least-Squares Analysis</subject><subject>Linear Models</subject><subject>Male</subject><subject>Medical engineering</subject><subject>Medical Engineering for Healthcare</subject><subject>Medicinsk teknik</subject><subject>Medicinsk teknik för hälsovård</subject><subject>Multivariate Analysis</subject><subject>Other technology</subject><subject>Partial least square</subject><subject>Physics</subject><subject>Prostate - pathology</subject><subject>Prostate cancer</subject><subject>prostate tissue</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Reproducibility of Results</subject><subject>Resonance sensor</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Stiffness</subject><subject>TECHNOLOGY</subject><subject>TEKNIKVETENSKAP</subject><subject>Övriga teknikvetenskaper</subject><issn>0309-1902</issn><issn>1464-522X</issn><issn>1464-522X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkk1vEzEQhi0EoqHwA7ggn7hAwB-73jVwicqnVIkLIG7WxDvbuPLaqe0lyr_HbSIQQmpPtjzPvHpnXhPylLNXnPXsNZNMc83qleumFYzdIwveqGbZCvHzPllc15cVECfkUc6XjDHRCPaQnHDNuepbtiBhFaib4AIpBPD77DKdsGziQMeY6DbFXKAgLS7nGan1kLMbnYXiYnhT6-jd5AKkPf0F3g0373TnyoYmzDFAsEgzhlzFahEekwcj-IxPjucp-f7xw7ezz8vzr5--nK3Ol1Z2rV7KEdoGhJC2Gy0o1LIbrEUrGmk1tD30PXAm5ZopVJLLYd0J1fZMA3DZ6F6ekpcH3bzD7bw221SHTHsTwZn37sfKxHRh5mk2vNdSV_zF3bgvs-m56ir9_EDX9VzNmIuZXLboPQSMczZKdZ1uW3UnKFgjRH9jlx9AWxeeE45_HHBmrqM2_0Vde54dxef1hMPfjmO2FXh3AFyoWU6wi8kPpsDexzSmmozLRt6m__af9g2CLxsLCc1lnFP9LfkWd78B8FfLLQ</recordid><startdate>2009</startdate><enddate>2009</enddate><creator>Lindberg, P. 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A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3759-3fa54a223c7fca6e937dccec243c9a58a88a1033b06e6313db7265809aa134983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithms</topic><topic>classification</topic><topic>Diagnostic Imaging - methods</topic><topic>Equipment Design</topic><topic>fysik</topic><topic>Humans</topic><topic>Image analysis</topic><topic>Image Processing, Computer-Assisted - instrumentation</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Least-Squares Analysis</topic><topic>Linear Models</topic><topic>Male</topic><topic>Medical engineering</topic><topic>Medical Engineering for Healthcare</topic><topic>Medicinsk teknik</topic><topic>Medicinsk teknik för hälsovård</topic><topic>Multivariate Analysis</topic><topic>Other technology</topic><topic>Partial least square</topic><topic>Physics</topic><topic>Prostate - pathology</topic><topic>Prostate cancer</topic><topic>prostate tissue</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Reproducibility of Results</topic><topic>Resonance sensor</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Stiffness</topic><topic>TECHNOLOGY</topic><topic>TEKNIKVETENSKAP</topic><topic>Övriga teknikvetenskaper</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lindberg, P. 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| 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 |
| title | An image analysis method for prostate tissue classification: preliminary validation with resonance sensor data |
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