Feature Selection for Automatic CT-based Prostate Segmentation

被引：0

作者：

Kos, Artur ^{[1
]}

Skalski, Andrzej ^{[2
]}

Zielinski, Tomasz P. ^{[1
]}

Gomes, Diana ^{[3
]}

Sa, Vitor ^{[3
]}

Kedzierawski, Piotr ^{[4
]}

Kuszewski, Tomasz ^{[5
]}

机构：

[1] AGH Univ Sci & Technol, Dept Telecommun, Krakow, Poland

[2] AGH Univ Sci & Technol, Dept Measurement & Elect, Krakow, Poland

[3] Univ Porto, Fac Engn, Oporto, Portugal

[4] Holly Cross Canc Ctr, Dept Radiotherapy, Kielce, Poland

[5] Holly Cross Canc Ctr, Dept Med Phys, Kielce, Poland

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST) | 2016年

关键词：

feature selection; prostate segmentation; computed tomography; radiotherapy;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper addresses a problem of feature selection for automatic prostate segmentation in Computed Tomography (CT) planning data for radiotherapy process. The following image descriptors have been tested in 2D and 3D scenarios: standard Hounsfield Unit (HU) profiles, histogram of oriented gradient (HoG), Haar wavelets, and Modality Independent Neighborhood Descriptor (MIND). The task was to distinguish a prostate interior and exterior. 22 CT volumes with different spatial resolution and different organ outlines have been used. The k-Nearest Neighbors (kNN) classifier was applied and the following recognition measures were evaluated, with 10-fold cross-validation: accuracy, precision, sensitivity and specificity.

引用

页码：243 / 248

页数：6

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