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BMAI

Biomedical Artificial
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Improving the Conspicuity of Nodules in Chest Radiographs by Use of Virtual Dual-Energy Radiography

Improving the Conspicuity of Nodules in Chest Radiographs by Use of Virtual Dual-Energy Radiography

We evaluated our virtual dual-energy radiography technique based on a massive-training artificial neural network (MTANN) in the improvement of the conspicuity of nodules in chest radiographs. To do this, we used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 Read more...

Research Area :
AIAD
Clinical Area :
BI
Anatomy :
Chest
Virtual Dual-Energy Radiography: Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive-Training Artificial Neural Network (MTANN)

Virtual Dual-Energy Radiography: Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive-Training Artificial Neural Network (MTANN)

When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this study, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multi-resolution massive-training artificial neural network (MTANN).< Read more...

Research Area :
AIAD
Clinical Area :
BI
Anatomy :
Chest
Reduction of False Positives in a CAD Scheme for Detection of Lung Nodules on MDCT by Use of 3D Massive-Training Artificial Neural Network

Reduction of False Positives in a CAD Scheme for Detection of Lung Nodules on MDCT by Use of 3D Massive-Training Artificial Neural Network

A major challenge in computer-aided diagnostic (CAD) schemes for lung nodule detection in multi-detector-row CT (MDCT) is to reduce false positives (FPs) while maintaining a high sensitivity level. Our purpose in this study was to develop a three-dimensional (3D) massive-training artificial neural network (MTANN) for reduction of FPs. To process quasi-isotropic voxels in the 3D MDCT volume, we Read more...

Research Area :
AIAD
Clinical Area :
BI
Anatomy :
Chest
Computerized Detection of Lung Nodules in Low-Dose CT, Part I: Basic Principle of Massive-Training Artificial Neural Network (MTANN) for Reduction of False Positives

Computerized Detection of Lung Nodules in Low-Dose CT, Part I: Basic Principle of Massive-Training Artificial Neural Network (MTANN) for Reduction of False Positives

We investigated a novel pattern-recognition technique based on an artificial neural network (ANN), called a massive-training artificial neural network (MTANN), for reduction of false positives (FPs) in computerized detection of lung nodules in low-dose CT. The MTANN consists of a linear-output multilayer ANN model, which is capable of operating on image data directly. The MTANN is trained by us Read more...

Research Area :
DML
Clinical Area :
BI
Anatomy :
Chest
Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes

Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes

Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes

We developed a novel temporal-subtraction (TS) technique combined with virtual dual-energyEtechnology for improved conspicuity of growing cancers and other pathologic changes in digital chest radiography (CXR). Digi Read more...

Research Area :
AII
Clinical Area :
BI
Anatomy :
Chest