The Mammographic Image Analysis Society digital mammogram database
01 Jan 1994-
About: The article was published on 1994-01-01 and is currently open access. It has received 900 citations till now.
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TL;DR: A digital image database of chest radiographs with and without a lung nodule was developed and showed that this database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.
Abstract: 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...
881 citations
TL;DR: The algorithm for feature selection is based on an application of a rough set method to the result of principal components analysis (PCA) used for feature projection and reduction.
801 citations
TL;DR: A new mammographic database built with full-field digital mammograms, which presents a wide variability of cases, and is made publicly available together with precise annotations is presented and can be a reference for future works centered or related to breast cancer imaging.
724 citations
01 Mar 2009
TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.
Abstract: 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.
564 citations
01 Dec 2016
TL;DR: It is shown that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficientDenoising of medical images.
Abstract: 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.
488 citations