Thyroid classification and segmentation in ultrasound images using machine learning algorithms

人工智能 支持向量机 计算机科学 分割 直方图 基本事实 模式识别(心理学) 图像分割 特征提取 分类器(UML) 特征(语言学) 超声波 机器学习 计算机视觉 放射科 图像(数学) 医学 哲学 语言学
作者
D. Selvathi,V.S. Sharnitha
标识
DOI:10.1109/icsccn.2011.6024666
摘要

The clinical reports usually offer morphometric data in terms of change relative to a prior study. Therefore, to provide the information about an object clinically in terms of its size and shape, image segmentation and classification are important tools in medical image processing. Ultrasound is a versatile imaging technique that can reveal the internal structure of organs, often with astounding clarity. Ultrasound is unique in its ability to image patient anatomy and physiology in real time, providing an important, rapid and non-invasive means of evaluation. Ultrasound continues to make significant contributions to patient care by reassuring patients and enhancing their quality of life by helping physicians understand their anatomy in ways not possible with other techniques. US imaging is thus one of the most commonly used auxiliary tools in clinical diagnosis. In this paper, an automatic system is developed that classifies the thyroid images and segments the thyroid gland using machine learning algorithms. The classifiers such as SVM, ELM are used. The features such as mean, variance, Coefficient of Local Variation Feature, Histogram Feature, NMSID Feature, and Homogeneity are extracted and these features are used to train the classifiers such as ELM and SVM. The results are compared with the ground truth images obtained from the radiologist and the performance measure such as accuracy is evaluated. It is observed that the segmentation using ELM is better than SVM classifier.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
如云发布了新的文献求助10
2秒前
angela给angela的求助进行了留言
2秒前
2秒前
XZZH完成签到,获得积分10
3秒前
3秒前
独特乘云发布了新的文献求助10
4秒前
WELXCNK发布了新的文献求助10
4秒前
setuin发布了新的文献求助10
5秒前
烟花应助aixue采纳,获得10
6秒前
CC完成签到,获得积分10
6秒前
tyh完成签到,获得积分10
6秒前
完美世界应助Huang采纳,获得10
7秒前
大李完成签到,获得积分10
7秒前
8秒前
8秒前
9秒前
乐乐乐乐乐乐应助大橙子采纳,获得10
9秒前
ylw发布了新的文献求助10
9秒前
独特乘云完成签到,获得积分10
10秒前
暮雨发布了新的文献求助10
10秒前
情怀应助zhaopeipei采纳,获得10
10秒前
10秒前
嘿嘿完成签到,获得积分20
11秒前
量子星尘发布了新的文献求助10
11秒前
11秒前
李健应助zhuxiaonian采纳,获得10
11秒前
星辰大海应助setuin采纳,获得10
11秒前
1335804518完成签到 ,获得积分10
12秒前
13秒前
14秒前
Seyn发布了新的文献求助10
15秒前
SciGPT应助ylw采纳,获得10
15秒前
典雅牛青发布了新的文献求助10
15秒前
16秒前
16秒前
科研通AI5应助lh961129采纳,获得10
16秒前
WJH发布了新的文献求助10
17秒前
18秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038657
求助须知:如何正确求助?哪些是违规求助? 3576306
关于积分的说明 11375198
捐赠科研通 3306108
什么是DOI,文献DOI怎么找? 1819379
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066