ABNORMAL REGION DETECTION IN GASTROSCOPIC IMAGES BY COMBINING CLASSIFIERS ON NEIGHBORING PATCHES

被引：11

作者：

Zhang, Su ^{[1
]}

Yang, Wei ^{[1
]}

Wu, Yi-Lun ^{[1
]}

Yao, Ru ^{[1
]}

Cheng, Shi-Dan ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Dept Biomed Engn, Shanghai 200240, Peoples R China

[2] Shanghai Jiao Tong Univ, Ruijin Hosp, Shanghai 200240, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Object detection; Ensemble learning; Classifier combination; Endoscopic image; SYSTEM;

D O I：

10.1109/ICMLC.2009.5212217

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Gastroscopy is widely used for the clinical examination of gastric diseases. The computerized methods capable to detect abnormal regions can help the physicians to identify the suspicious regions in gastroscopic images. The patch-based technique with the boosted stumps is adopted to detect all kinds of abnormalities in this paper. Considering that the responses of patch classifiers on the neighboring image patches are coherent, a flexible detection model is proposed which combines the patch classifiers' outputs in the products of experts form to enhance the coherence of patch classifiers. The detection methods are evaluated on a large gastroscopic image dataset containing 2949 images of 413 patients. Experimental results show that the proposed method can improve the detection performance.

引用

页码：2374 / +

页数：2

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