OBJECTIVE. We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis.MATERIALS AND METHODS. One hundred and fifty-four conventional chest radiographs with a lung nodule and 93 radiographs without a nodule were selected from 14 medical centers and were digitized by a laser digitizer with a 2048 × 2048 matrix size (0.175-mm pixels) and a 12-bit gray scale. Lung nodule images were classified into five groups according to the degrees of subtlety shown. The observations of 20 participating radiologists were subjected to ROC analysis for detecting solitary pulmonary nodules. Experimental results (areas under the curve, Az) obtained from observer...

Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

Rough set methods in feature selection and recognition

INbreast: toward a full-field digital mammographic database.

Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.

Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.

Medical Image Denoising Using Convolutional Denoising Autoencoders

The Mammographic Image Analysis Society digital mammogram database

The Mammographic Image Analysis Society database of digital mammograms (v1.21). Contains the original 322 images (161 pairs) at 50 micron resolution in "Portable Gray Map" (PGM) format and associated truth data.

J. Parker

Papers

The Mammographic Image Analysis Society digital mammogram database

Mammographic Image Analysis Society (MIAS) database v1.21