MIPP: A Medical Image Processing Platform based on ITK, VTK and Eclipse RCP

Swoboda, Roland; Zwettler, Gerald; Pfeifer, Franz; Backfrieder, Werner

doi:10.54294/jlx9vt

Cited by 1 publication

(1 citation statement)

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“…For I/O operations and random number generation, boost library (Boost, 2014) is utilized as external library. Although, the core boost concepts are meanwhile added to C++11 standard, the utilization of boost programming paradigms started with the development of the MIPP framework (Swoboda, Backfrieder, Zwettler, & Pfeifer, 2008) was perpetuated for reasons of consistency. Besides, no further external code was utilized.…”

Section: Methodsmentioning

confidence: 99%

Automated Domain-Specific Feature Selection for Classification-based Segmentation of Tomographic Medical Image Data

Zwettler

Backfrieder

2017

International Journal of Privacy and Health Information Management

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Classification-based segmentation is an approach to establish generic analysis of medical image data. Significant feature sets covering different characteristics of regions to segment allow for robust discrimination of topologically defined classes. In this work a method for automated domain-specific feature selection to achieve a higher level of predictability is presented, incorporating multivariate feature analysis. For calculation of the probability density function, different approaches, like histogram analysis, enumeration of the entire feature space or umbrella Monte Carlo Integration are investigated. Furthermore, meta features calculated on entire classification results rather than on particular regions are introduced. Predictability of both, single local and meta features, is evaluated for different medical datasets as well for simulated intensity volumes, allowing testing and evaluating specific classification problems. The automated feature selection proofs to be accurate for classification-based segmentation utilizing well-known machine learning approaches.

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Section: Methodsmentioning

confidence: 99%