RATIONALE AND OBJECTIVE We developed a technique that uses a multiple massive-training artificial neural network (multi-MTANN) to reduce the number of false-positive results in a computer-aided diagnostic (CAD) scheme for detecting nodules in chest radiographs. MATERIALS AND METHODS Our database consisted of 91 solitary pulmonary nodules, including 64 malignant nodules and 27 benign nodules, ...

We developed a method for suppression of the contrast of ribs in chest radiographs by means of a massive training artificial neural network (MTANN). The MTANN is a trainable highly nonlinear filter that can be trained by using input chest radiographs and the corresponding teacher images. We used either the soft-tissue image or the bone image obtained by use of a dual-energy subtraction techniqu...

Conventional chest radiography poses a challenging technical problem because of its requirement for simultaneous high-contrast display and wide-latitude recording across the entire image. We developed and evaluated a method of producing chest radiographs by using a tantalum air-interspace grid for highly efficient scatter rejection, wide-latitude X-ray film for recording the low-scatter image, ...

Bernd Menser, PhD, X-Ray Scientist Bedside chest radiographs can be acquired with or without an anti-scatter grid. Using a grid may improve image contrast, although it is frequently avoided in clinical practice, due to dose considerations, beam alignment difficulties, and a more complicated workflow[2]. Also, structures appearing from the (usually stationary) grid may interfere with diagnostic ...

Introduction: The study was undertaken to evaluate the practice of chest radiography using different digital imaging systems and its influence on dose and image quality.  Materials and Methods: The study was carried out in two hospitals from March 2016 to June 2016. Sixty ambulatory patients aged 21 to 60 years who were able to cooperate without difficulty and weighed between 60 to 80 kg were ...

We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of de...

We have been developing a computer-aided detection (CAD) scheme for pneumoconiosis based on a rule-based plus artificial neural network (ANN) analysis of power spectra. In this study, we have developed three enhancement methods for the abnormal patterns to reduce false-positive and false-negative values. The image database consisted of 2 normal and 15 abnormal chest radiographs. The Internation...