Abstract
The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes.
Highlights
With respect to the last decade, ten times more medical images are taken, increasing the number of images per body region per patient to 200–1000 [1]
This paper evaluates the effect of several image enhancement techniques on the prediction accuracy rate on radiographs
As computer-aided assistance is needed in image interpretation [19] and improved prediction accuracies have been obtained using deep convolutional neural networks [7], the objective of this paper is to create an automatic image annotation system using deep learning and image enhancement techniques
Summary
With respect to the last decade, ten times more medical images are taken, increasing the number of images per body region per patient to 200–1000 [1]. This huge increase can be traced back to two major facts: rapid advances in technology and significant importance of medical images. Medical images contain relevant information that is valuable to physicians. It provides a reliable source of anatomical and functional information for accurate diagnosis, effective treatment planning as well as research work [2, 3].
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