Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

Suzuki, Kenji; Abé, Hiroyuki; MacMahon, Heber; Doi, Kunio

doi:10.1109/tmi.2006.871549

Cited by 212 publications

(161 citation statements)

References 64 publications

Supporting

Mentioning

157

Contrasting

Order By: Relevance

“…FP reduction in CAD for polyp detection in CT colonography [35,[41][42][43][44]. Bone separation from soft tissue in CXR [36,37]. Enhancement of lung nodules in CT [38].…”

Section: Convolution Neural Network (Including Shiftinvariant Neuralmentioning

confidence: 99%

Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

Suzuki¹

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

View full text Add to dashboard Cite

“…FP reduction in CAD for polyp detection in CT colonography [35,[41][42][43][44]. Bone separation from soft tissue in CXR [36,37]. Enhancement of lung nodules in CT [38].…”

Section: Convolution Neural Network (Including Shiftinvariant Neuralmentioning

confidence: 99%

Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

Suzuki¹

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

View full text Add to dashboard Cite

“…Adaptability and nonlinearity of ANNs has made them well-known, easy to implement, useful tools in pattern recognition [7,8], as well as in medical image processing [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A neural network learned information measures for heart motion abnormality detection

Nambakhsh

Punithakumar²,

Ayed

et al. 2011

SPIE Proceedings

View full text Add to dashboard Cite

Abstract. In this study, we propose an information theoretic neural network for normal/abnormal left ventricular motion classification which outperforms significantly other recent methods in the literature. The proposed framework consists of a supervised 3-layer artificial neural network (ANN) which uses hyperbolic tangent sigmoid and linear transfer functions for hidden and output layers, respectively. The ANN is fed by information theoretic measures of left ventricular wall motion such as Shannon's differential entropy (SDE), Rényi entropy and Fisher information, which measure global information of subjects distribution. Using 395×20 segmented LV cavities of short-axis magnetic resonance images (MRI) acquired from 48 subjects, the experimental results show that the proposed method outperforms Support Vector Machine (SVM) and thresholding based information theoretic classifiers. It yields a specificity equal to 90%, a sensitivity of 91%, and a remarkable Area Under Curve (AUC) for Receiver Operating Characteristic (ROC), equal to 93.2%.

show abstract

“…ANN has great success in many applications including pattern classification, decision making, forecasting, and adaptive control. Many research studies have been carried out in the medical field utilising ANN for medical image segmentation and classification with different medical image modalities [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent approach for PET volume analysis

Sharif¹,

Amira²,

Zaidi

2010

Proceedings of 2010 IEEE International Symposium on Circuits and Systems

View full text Add to dashboard Cite

Abstract-Tumour classification and quantification in positron emission tomography (PET) imaging at early stage of illness are important for radiotherapy planning, tumour diagnosis, and fast recovery. Analysing large medical volumes using traditional techniques requires a decent amount of time, and in some approaches poor accuracy is achieved. Artificial intelligence (AI) technologies can provide better accuracy and save decent amount of time. Artificial neural network (ANN), as one of the best AI technologies, has the capability to classify, measure precisely the region of interest, and model the clinical evaluation for a specific problem. This paper presents a novel application of the ANN in the wavelet domain for PET volume segmentation. ANN performance evaluation using different number of hidden neurons is also considered. The proposed intelligent system outputs are compared with the outputs of thresholding, and clustering based approaches. Two PET phantom data sets and real PET volumes have been utilised to validate the proposed system which has shown promising results.

show abstract

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

Cited by 212 publications

References 64 publications

Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

A neural network learned information measures for heart motion abnormality detection

Intelligent approach for PET volume analysis

Contact Info

Product

Resources

About