Boundary detection of retinoblastoma tumors with neural networks

被引：4
|
作者：
Chai, MIB [1 ]
Chai, A [1 ]
Sullivan, P [1 ]
机构：
[1] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON M5S 3G8, Canada
来源：
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS | 2001年 / 25卷 / 03期
基金：
加拿大自然科学与工程研究理事会;
关键词：
neural networks; soft competitive learning; retinoblastoma; ultrasonography; tumor; eye tumor; volume measurement; EM algorithm;
D O I：
10.1016/S0895-6111(00)00076-8
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Consistent and accurate measurement of retinoblastoma tumors is of important clinical value for treatment management. This paper presents an algorithm for the determination of retinoblastoma (RB) tumor to assist in the determination of tumor volume changes throughout treatment periods. The result of the development of a neural network approach for the analysis of three-dimensional ultrasound images shows that it is possible to identify retinoblastoma tumors and accurately determine the front and back boundary of the tumor. The algorithm used was a soft competitive learning network with two inputs. The outputs of the network identify the eye, the tumor, and the back of the eye. (C) 2001 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：257 / 264
页数：8
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