2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI)最新文献

Combining one-class classifiers for imbalanced classification of breast thermogram features 结合单类分类器对乳房热像特征的不平衡分类

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583855

B. Krawczyk, G. Schaefer, Michal Wozniak

{"title":"Combining one-class classifiers for imbalanced classification of breast thermogram features","authors":"B. Krawczyk, G. Schaefer, Michal Wozniak","doi":"10.1109/CIMI.2013.6583855","DOIUrl":"https://doi.org/10.1109/CIMI.2013.6583855","url":null,"abstract":"Thermography provides an interesting modality for diagnosing breast cancer as it is a non-contact, non-invasive and passive technique that is able to detect small tumors, which in turn can lead to earlier diagnosis. We perform computer-aided diagnosis of breast thermograms based on image features describing bilateral differences in regions of interest and a pattern classification approach that learns from previous examples. As is often the case in medical diagnosis, such training sets are imbalanced as typically (many) more benign cases get recorded compared to malignant cases. In this paper, we address this problem and perform classification using an ensemble of one-class classifiers. One-class classification uses samples from a single distribution to derive a decision boundary, and employing this method on the minority class can significantly boost its recognition rate and hence the sensitivity of our approach. We combine several one-class classifiers using a random subspace approach and a diversity measure to select members of the committee. We show that our proposed technique works well and leads to significantly improved performance compared to a single one-class predictor as well as compared to state-of-the-art classifier ensembles for imbalanced data.","PeriodicalId":374733,"journal":{"name":"2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132366459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Lung nodule detection in CT images using neuro fuzzy classifier 基于神经模糊分类器的CT图像肺结节检测

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583857

A. Tariq, M. U. Akram, Muhammad Younus Javed

引用次数: 80

Phantom-based evaluation of isotropic reconstruction of 4-D MRI volumes using super-resolution 基于幻影的超分辨率四维MRI体各向同性重建评价

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583851

E. V. Reeth, I. Tham, Cher Heng Tan, C. Poh

{"title":"Phantom-based evaluation of isotropic reconstruction of 4-D MRI volumes using super-resolution","authors":"E. V. Reeth, I. Tham, Cher Heng Tan, C. Poh","doi":"10.1109/CIMI.2013.6583851","DOIUrl":"https://doi.org/10.1109/CIMI.2013.6583851","url":null,"abstract":"This article investigates the feasibility of isotropic super-resolution reconstruction on 4-D (3-D + time) thoracic MRI data. 4-D MRI sequences generally have high temporal resolution to characterize dynamic phenomena but poor spatial resolution, creating highly anisotropic voxels elongated in the slice-select dimension. Isotropic post-acquisition reconstruction can be obtained using super-resolution algorithms. A new MRI compatible phantom design that simulates lung tumour motion is introduced to evaluate the feasibility and performance of the proposed super-resolution algorithm in the context of 4-D MRI. Several orthogonal low-resolution acquisitions of the phantom are performed through time using a fast true 3-D gradient echo based sequence. The acquired volumes are then registered and combined using a total-variation based regularizer super-resolution algorithm to obtain the high-resolution volume. The quality of the reconstruction is evaluated by measuring the mutual information between the reconstructed volume and a direct isotropic 3-D acquisition. Subjective and objective evaluations show the superiority of our approach compared to the averaging method. This article also discusses the influence of various parameters such as the number of low-resolution scans used and the influence of automatic motion estimation versus known displacement.","PeriodicalId":374733,"journal":{"name":"2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A priori knowledge based deformable surface model for newborn brain MR image segmentation 基于先验知识的新生儿脑MR图像分割可变形曲面模型

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583850

Syoji Kobashi, Aya Hashioka, Yuki Wakata, K. Ando, R. Ishikura, Kei Kuramoto, T. Ishikawa, S. Hirota, Y. Hata

引用次数: 2

Computer aided diagnostic system for grading of diabetic retinopathy 糖尿病视网膜病变分级的计算机辅助诊断系统

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583854

A. Tariq, M. Akram, M. Javed

引用次数: 11

Detection of onset of Alzheimer's disease from MRI images using a GA-ELM-PSO classifier 使用GA-ELM-PSO分类器从MRI图像中检测阿尔茨海默病的发病

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583856

S. Saraswathi, B. S. Mahanand, A. Kloczkowski, S. Sundaram, N. Sundararajan

{"title":"Detection of onset of Alzheimer's disease from MRI images using a GA-ELM-PSO classifier","authors":"S. Saraswathi, B. S. Mahanand, A. Kloczkowski, S. Sundaram, N. Sundararajan","doi":"10.1109/CIMI.2013.6583856","DOIUrl":"https://doi.org/10.1109/CIMI.2013.6583856","url":null,"abstract":"In this paper, a novel method for detecting the onset of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is presented. It uses a combination of three different machine learning algorithms in order to get improved results and is based on a three-class classification problem. The three classes for classification considered in this study are normal, very mild AD and mild and moderate AD subjects. The machine learning algorithms used are: the Extreme Learning Machine (ELM) for classification, with its performance optimized by a Particle Swarm Optimization (PSO) and a Genetic algorithm (GA) used for feature selection. A Voxel-Based Morphometry (VBM) approach is used for feature extraction from the MRI images and GA is used to reduce the high dimensional features needed for classification. The GA-ELM-PSO classifier yields an average training accuracy of 94.57 % and a testing accuracy of 87.23 %, averaged across the three classes, over ten random trials. The results clearly indicate that the proposed approach can differentiate between very mild AD and normal cases more accurately, indicating its usefulness in detecting the onset of AD.","PeriodicalId":374733,"journal":{"name":"2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI)","volume":"2020 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Genetic algorithms for Voxel-based medical image registration 基于体素的医学图像配准遗传算法

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583853

A. Valsecchi, S. Damas, J. Santamaría, L. Marrakchi-Kacem

引用次数: 21

A fully automatic probabilistic 3D approach for the detection and assessment of pleural thickenings from CT data 从CT数据中检测和评估胸膜增厚的全自动概率三维方法

2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI) Pub Date : 2013-04-16 DOI: 10.1109/CIMI.2013.6583852

K. Chaisaowong, Chaicharn Akkawutvanich, C. Wilkmann, T. Kraus

引用次数: 4