Content-based Retrieval of Medical Images by Paulo Mazzoncini de Azevedo-Marques, Rangaraj Rangayyan
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Content-based Retrieval of Medical Images
Author : Paulo Mazzoncini de Azevedo-Marques, Rangaraj Rangayyan
Publisher : Springer Nature
Published : 2022-06-01
ISBN-10 : 3031016513
ISBN-13 : 9783031016516
Number of Pages : 125 Pages
Language : en
Descriptions Content-based Retrieval of Medical Images
Content-based image retrieval (CBIR) is the process of retrieval of images from a database that are similar to a query image, using measures derived from the images themselves, rather than relying on accompanying text or annotation. To achieve CBIR, the contents of the images need to be characterized by quantitative features; the features of the query image are compared with the features of each image in the database and images having high similarity with respect to the query image are retrieved and displayed. CBIR of medical images is a useful tool and could provide radiologists with assistance in the form of a display of relevant past cases. One of the challenging aspects of CBIR is to extract features from the images to represent their visual, diagnostic, or application-specific information content. In this book, methods are presented for preprocessing, segmentation, landmarking, feature extraction, and indexing of mammograms for CBIR. The preprocessing steps include anisotropic diffusion and the Wiener filter to remove noise and perform image enhancement. Techniques are described for segmentation of the breast and fibroglandular disk, including maximum entropy, a moment-preserving method, and Otsu's method. Image processing techniques are described for automatic detection of the nipple and the edge of the pectoral muscle via analysis in the Radon domain. By using the nipple and the pectoral muscle as landmarks, mammograms are divided into their internal, external, upper, and lower parts for further analysis. Methods are presented for feature extraction using texture analysis, shape analysis, granulometric analysis, moments, and statistical measures. The CBIR system presented provides options for retrieval using the Kohonen self-organizing map and the k-nearest-neighbor method. Methods are described for inclusion of expert knowledge to reduce the semantic gap in CBIR, including the query point movement method for relevance feedback (RFb). Analysis of performance is described in terms of precision, recall, and relevance-weighted precision of retrieval. Results of application to a clinical database of mammograms are presented, including the input of expert radiologists into the CBIR and RFb processes. Models are presented for integration of CBIR and computer-aided diagnosis (CAD) with a picture archival and communication system (PACS) for efficient workflow in a hospital. Table of Contents: Introduction to Content-based Image Retrieval / Mammography and CAD of Breast Cancer / Segmentation and Landmarking of Mammograms / Feature Extraction and Indexing of Mammograms / Content-based Retrieval of Mammograms / Integration of CBIR and CAD into Radiological Workflow
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Results Content-based Retrieval of Medical Images
CNN-Based Pill Image Recognition for Retrieval Systems - CBIR is the field that describes automated image retrieval techniques that are capable of identifying images based on their "content", , features embedded in the image, such as shape, texture, and color [2,3,4]. Research is still ongoing to improve the effectiveness of CBIR in terms of extracting primitive features (color and shape) and
Term dependency extraction using Rule-based Bayesian Network for - Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in retrieving medical images with textual descriptions. Usually, these descriptions are very brief and cannot express the whole visual content of the image in words, hence negatively affect the retrieval performance
Content-based 3D neuroradiologic image retrieval: Preliminary results - A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval
(PDF) Content-Based Medical Image Retrieval - ResearchGate - The content-based retrieval is based on the image visual content information, which automatically extracts the rich visual properties / features to characterize the images [10] [11] [12]. While
Content-Based Image Retrieval - an overview - ScienceDirect - In content-based image retrieval for medical diagnosis, it is essential to select the images that present higher similarity with a given query image. In this context, despite some efforts in the literature, most approaches do not take into account constraints inherent to medical applications, which demand faster and more effective learning
A Content-Based Medical Image Retrieval Algorithm - However, retrieval of relevant medical images out of large-scale databases effectively remains a challenging problem. The primary issues for retrieving an image from the medical repository are the large time taken in retrieving the image, low precision, and recall rates of the retrieval process. This paper proposes a content-based image
Class-driven content-based medical image retrieval using hash codes of - A new content-based medical image retrieval (CBMIR) framework using CNN and hash coding is proposed, which adopts a Siamese network in which pairs of images are used as inputs, and a model is learned to make images belonging to the same class have similar features by using weight sharing and a contrastive loss function
A deep neural network model for content-based medical image retrieval - The CNN model is trained for content-based medical image retrieval for radiography images, where the features learned from the dense layers are used for improving retrieval. A similarity measure is employed to obtain image indexes, which are used to generate a ranked list of top-k similar images during retrieval. The key contributions of this
Interpretability-Guided Content-Based Medical Image Retrieval - However, general content-based image retrieval systems are often not helpful in the context of medical imaging since they do not consider the fact that relevant information in medical images is typically spatially constricted. In this work, we explore the use of interpretability methods to localize relevant regions of images, leading to more
Content-Based Medical Image Retrieval and Intelligent Interactive - Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care Diagnostics (Basel) . 2021 Aug 13;11(8):1470. doi: 10.3390/diagnostics11081470
Content-based medical image retrieval: a survey of applications to - Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data J Digit Imaging. 2013 Dec;26 ... Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search
Content-Based Image Retrieval for Medical Image | IEEE Conference - Content-Based Image Retrieval for Medical Image. Abstract: In this paper, the SIMPLIcity (Semantics-sensitive Integrate Matching for Picture Libraries), an image retrieval system is introduced. The feature extraction is based on Histogram, color layout and coefficients of wavelet transform. This retrieving system adopts feature database for
Midovsky/cbir-with-python: Content based image retrieval - Github - Content based image retrieval. Contribute to Midovsky/cbir-with-python development by creating an account on GitHub
Integrating images to patient electronic medical records through - Integrating Images to Patient Electronic Medical Records through Content-based Retrieval Techniques Agma Traina1, Natália A. Rosa2, Caetano Traina Jr.1 1 Computer Science Department - University of São Paulo at São Carlos - Brazil 2 Center for Science of Image and Medical Physics - Medical School of Ribeirão Preto - University of São Paulo at Ribeirão Preto - Brazil agma@
PDF Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval - adversarial examples for content-based image retrieval," International Joint Conference on Neural Networks, pp. 1-9, 2022. [9]S. Hamreras, B. Boucheham, M. A. Molina-Cabello, R. Ben´ıtez-Rochel, and E. L ´opez-Rubio, "Dynamic se-lection of classifiers for content based image retrieval," International Joint Conference on Neural Networks,
[2303.03633] Sketch-based Medical Image Retrieval - The amount of medical images stored in hospitals is increasing faster than ever; however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors; nevertheless, example images cannot always be prepared. Besides, there can be images with rare
CBMIR: Content-based Image Retrieval Algorithm for Medical Image - Content-based image retrieval (CBIR) applies to techniques for retrieving similar images from image databases, based on automated feature extraction methods. In recent years, the medical imaging field has been grown and is generating a lot more interest in methods and tools, to control the analysis of medical images
Content-based Retrieval of Medical Images: Landmarking ... - ResearchGate - Content-based medical image retrieval (CBMIR) is a prospective technology through which a defined approach could recover past cases based on image patterns [6, 7]. In CBMIR systems, an image can
Content-based medical image retrieval: a survey of applications to -
Content-Based Image Retrieval for Medical Image | IEEE … - Web · In this paper, the SIMPLIcity (Semantics-sensitive Integrate Matching for Picture Libraries), an image retrieval system is introduced. The feature extraction is …
Content Based Medical Image Retrieval Based on Salient … - Web · In traditional text based medical image retrieval system, it is hard to find visually similar images in large medical image database. Content-based image …
Content-based image retrieval in medical applications - PubMed - WebObjectives: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its …
Content-Based Medical Image Retrieval and Intelligent Interactive - Web · Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care Diagnostics (Basel) . 2021 Aug …
CBMIR (Content-based medical image retrieval) - Github - WebCBMIR (Content-based medical image retrieval) Image similarity search for radiology images with BoVW (Bag-of-Visual-Words), HOG and CNN-extracted descriptors. The …
A Content-Based Medical Image Retrieval Algorithm - Web · However, retrieval of relevant medical images out of large-scale databases effectively remains a challenging problem. The primary issues for retrieving an image …
Content-based medical image retrieval using a novel … -
A Panoramic View of Content-based Medical Image Retrieval system - Web · Abstract: Content-Based Image Retrieval (CBIR) system for medical applications is one of the trending research fields in computer vision and Digital image …
Content-Based Image Retrieval in Medical Domain: A Review -
Implementing Relevance Feedback for Content-Based Medical Image Retrieval - Content-based image medical retrieval (CBMIR) is a technique for retrieving medical images on the basis of automatically derived image features such as colour, texture and shape. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. The retrieval performance of a CBMIR system depends mainly on the representation of image features, which researchers have studied extensively for decades. Although a number of methods and approaches have been suggested, it remains one of the most challenging problems in current (CBMIR) studies, largely due to the well-known “semantic gap” issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts. There have been many techniques proposed to bridge this gap. This study proposes a novel relevance feedback retrieval method (RFRM) for CBMIR. The feedback implemented here is based on voting values performed by each class in the image repository. Here, eighteen using colo
Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data - PubMed - Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-v …
CBMIR: Content-based Image Retrieval Algorithm for Medical Image Databases - We propose a novel algorithm for the retrieval of images from medical image databases by content. The aim of this article is to present a content-based retrieval algorithm that is robust to scaling, with translation of objects within an image. For the
A new method of content based medical image retrieval and its applications to CT imaging sign retrieval - This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse th…
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval - Deep neural networks have been investigated in learning latentrepresentations of medical images, yet most of the studies limit their approachin a single supervised convolutional neural network (CNN), which usually relyheavily on a large scale annotated dataset for training. To learn imagerepresentations with less supervision involved, we propose a deep Siamese CNN(SCNN) architecture that can be trained with only binary image pairinformation. We evaluated the learned image representations on a task ofcontent-based medical image retrieval using a publicly available multiclassdiabetic retinopathy fundus image dataset. The experimental results show thatour proposed deep SCNN is comparable to the state-of-the-art single supervisedCNN, and requires much less supervision for training
Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval - Medical images can be decomposed into normal and abnormal features, which isconsidered as the compositionality. Based on this idea, we propose anencoder-decoder network to decompose a medical image into two discrete latentcodes: a normal anatomy code and an abnormal anatomy code. Using these latentcodes, we demonstrate a similarity retrieval by focusing on either normal orabnormal features of medical images
Content-based image retrieval in medical applications - PubMed - Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves c …
Content-based medical image retrieval using a novel hybrid scattering coefficients - bag of visual words - DWT relevance fusion - Multimedia Tools and Applications - Image content analysis plays a major role in image classification, retrieval, and indexing together with object and scene recognition. Numerous image
Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence - Medical-image-based diagnosis is a tedious task‚ and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However,
Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care - PubMed - Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, a …