67,335 research outputs found
A MEDICAL X-RAY IMAGE CLASSIFICATION AND RETRIEVAL SYSTEM
Medical image retrieval systems have gained high interest in the scientific community due to the advances in medical imaging technologies. The semantic gap is one of the biggest challenges in retrieval from large medical databases. This paper presents a retrieval system that aims at addressing this challenge by learning the main concept of every image in the medical database. The proposed system contains two modules: a classification/annotation and a retrieval module. The first module aims at classifying and subsequently annotating all medical images automatically. SIFT (Scale Invariant Feature Transform) and LBP (Local Binary Patterns) are two descriptors used in this process. Image-based and patch-based features are used as approaches to build a bag of words (BoW) using these descriptors. The impact on the classification performance is also evaluated. The results show that the classification accuracy obtained incorporating image-based integration techniques is higher than the accuracy obtained by other techniques. The retrieval module enables the search based on text, visual and multimodal queries. The text-based query supports retrieval of medical images based on categories, as it is carried out via the category that the images were annotated with, within the classification module. The multimodal query applies a late fusion technique on the retrieval results obtained from text-based and image-based queries. This fusion is used to enhance the retrieval performance by incorporating the advantages of both text-based and content-based image retrieval
MRI brain classification using support vector machine
The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed
Multimorbidity Content-Based Medical Image Retrieval Using Proxies
Content-based medical image retrieval is an important diagnostic tool that
improves the explainability of computer-aided diagnosis systems and provides
decision making support to healthcare professionals. Medical imaging data, such
as radiology images, are often multimorbidity; a single sample may have more
than one pathology present. As such, image retrieval systems for the medical
domain must be designed for the multi-label scenario. In this paper, we propose
a novel multi-label metric learning method that can be used for both
classification and content-based image retrieval. In this way, our model is
able to support diagnosis by predicting the presence of diseases and provide
evidence for these predictions by returning samples with similar pathological
content to the user. In practice, the retrieved images may also be accompanied
by pathology reports, further assisting in the diagnostic process. Our method
leverages proxy feature vectors, enabling the efficient learning of a robust
feature space in which the distance between feature vectors can be used as a
measure of the similarity of those samples. Unlike existing proxy-based
methods, training samples are able to assign to multiple proxies that span
multiple class labels. This multi-label proxy assignment results in a feature
space that encodes the complex relationships between diseases present in
medical imaging data. Our method outperforms state-of-the-art image retrieval
systems and a set of baseline approaches. We demonstrate the efficacy of our
approach to both classification and content-based image retrieval on two
multimorbidity radiology datasets
An Efficient CBIR System for Medical Images Using Neural Network
This paper introduces an innovative Content-Based Image Retrieval (CBIR) system that has been specifically developed for medical databases. Its objective is to resolve the drawbacks of conventional keyword-based search approaches when considering the widespread digitization of medical illustrations, diagrams, and paintings. In contrast to conventional methods that rely on textual queries, CBIR systems effectively locate and retrieve relevant images by analyzing image content using computer vision and image processing techniques, as well as information retrieval and database methods.A key challenge in CBIR lies in bridging the semantic gap between high-level user queries, often expressed through example images, and the low-level features of images such as texture, shape, and objects. This paper explores techniques to mitigate this disparity, enhancing the system's ability to accurately interpret user queries and retrieve relevant images.
The proposed CBIR system operates within a medical database containing images of various human organs, including the brain, heart, hand, chest, spine, and shoulder, categorized into six distinct classes. By leveraging low-level image features such as texture and shape, extracted using methods like mean, variance, standard deviation, area, perimeter, circularity, and aspect ratio analysis, the system performs iterative searches to retrieve relevant images.Classification of retrieved images is accomplished using Artificial Neural Networks (ANN), which have demonstrated efficacy in medical image classification tasks based on imaging modalities and the presence of normal or abnormal conditions. Performance evaluation of the CBIR system is conducted using confusion matrices to calculate precision and recall, essential metrics for assessing retrieval accuracy.
By focusing on medical datasets and integrating advanced feature extraction and classification techniques, this CBIR system aims to significantly enhance image retrieval efficiency and accuracy, particularly in the context of medical applications where precise retrieval of relevant images is critical for diagnostic and research purposes.
 
Autoencoding the Retrieval Relevance of Medical Images
Content-based image retrieval (CBIR) of medical images is a crucial task that
can contribute to a more reliable diagnosis if applied to big data. Recent
advances in feature extraction and classification have enormously improved CBIR
results for digital images. However, considering the increasing accessibility
of big data in medical imaging, we are still in need of reducing both memory
requirements and computational expenses of image retrieval systems. This work
proposes to exclude the features of image blocks that exhibit a low encoding
error when learned by a autoencoder (). We examine the
histogram of autoendcoding errors of image blocks for each image class to
facilitate the decision which image regions, or roughly what percentage of an
image perhaps, shall be declared relevant for the retrieval task. This leads to
reduction of feature dimensionality and speeds up the retrieval process. To
validate the proposed scheme, we employ local binary patterns (LBP) and support
vector machines (SVM) which are both well-established approaches in CBIR
research community. As well, we use IRMA dataset with 14,410 x-ray images as
test data. The results show that the dimensionality of annotated feature
vectors can be reduced by up to 50% resulting in speedups greater than 27% at
expense of less than 1% decrease in the accuracy of retrieval when validating
the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image
Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015,
Orleans, Franc
Semi–Supervised Learning for Image Modality Classification
Searching for medical image content is a regular task for many physicians, especially in radiology. Retrieval of medical images from the scientific literature can benefit from automatic modality classification to focus the search and filter out non–relevant items. Training datasets are often unevenly distributed regarding the classes resulting sometimes in a less than optimal classification performance. This article proposes a semi–supervised learning approach applied using a k–Nearest Neighbour (k–NN) classifier to exploit unlabelled data and to expand the training set. The algorithmic implementation is described and the method is evaluated on the ImageCLEFmed modality classification benchmark. Results show that this approach achieves an improved performance over supervised k–NN and Random Forest classifiers. Moreover, medical case–based retrieval benefits from the modality filter
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