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Mahesh DB, Madhuri B, Lakshmi D R. Integration of optimized local directional weber pattern with faster region convolutional neural network for enhanced medical image retrieval and classification. Comput Intell 2022. [DOI: 10.1111/coin.12506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Zheng M, Geng Y, Li Q. Revisiting Local Descriptors via Frequent Pattern Mining for Fine-Grained Image Retrieval. ENTROPY (BASEL, SWITZERLAND) 2022; 24:156. [PMID: 35205452 PMCID: PMC8871172 DOI: 10.3390/e24020156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 11/17/2022]
Abstract
Fine-grained image retrieval aims at searching relevant images among fine-grained classes given a query. The main difficulty of this task derives from the small interclass distinction and the large intraclass variance of fine-grained images, posing severe challenges to the methods that only resort to global or local features. In this paper, we propose a novel fine-grained image retrieval method, where global-local aware feature representation is learned. Specifically, the global feature is extracted by selecting the most relevant deep descriptors. Meanwhile, we explore the intrinsic relationship of different parts via the frequent pattern mining, thus obtaining the representative local feature. Further, an aggregation feature that learns global-local aware feature representation is designed. Consequently, the discriminative ability among different fine-grained classes is enhanced. We evaluate the proposed method on five popular fine-grained datasets. Extensive experimental results demonstrate that the performance of fine-grained image retrieval is improved with the proposed global-local aware representation.
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Affiliation(s)
| | | | - Qingyong Li
- Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China; (M.Z.); (Y.G.)
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Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network. BIOMED RESEARCH INTERNATIONAL 2021; 2020:6687733. [PMID: 33426062 PMCID: PMC7781707 DOI: 10.1155/2020/6687733] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 12/07/2020] [Accepted: 12/14/2020] [Indexed: 11/17/2022]
Abstract
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.
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Dipta SR, Taherzadeh G, Ahmad MW, Arafat ME, Shatabda S, Dehzangi A. SEMal: Accurate protein malonylation site predictor using structural and evolutionary information. Comput Biol Med 2020; 125:104022. [PMID: 33022522 DOI: 10.1016/j.compbiomed.2020.104022] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 10/23/2022]
Abstract
Post Transactional Modification (PTM) is a vital process which plays an important role in a wide range of biological interactions. One of the most recently identified PTMs is Malonylation. It has been shown that Malonylation has an important impact on different biological pathways including glucose and fatty acid metabolism. Malonylation can be detected experimentally using mass spectrometry. However, this process is both costly and time-consuming which has inspired research to find more efficient and fast computational methods to solve this problem. This paper proposes a novel approach, called SEMal, to identify Malonylation sites in protein sequences. It uses both structural and evolutionary-based features to solve this problem. It also uses Rotation Forest (RoF) as its classification technique to predict Malonylation sites. To the best of our knowledge, our extracted features as well as our employed classifier have never been used for this problem. Compared to the previously proposed methods, SEMal outperforms them in all metrics such as sensitivity (0.94 and 0.89), accuracy (0.94 and 0.91), and Matthews correlation coefficient (0.88 and 0.82), for Homo Sapiens and Mus Musculus species, respectively. SEMal is publicly available as an online predictor at: http://brl.uiu.ac.bd/SEMal/.
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Affiliation(s)
- Shubhashis Roy Dipta
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Ghazaleh Taherzadeh
- Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA
| | - Md Wakil Ahmad
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Md Easin Arafat
- Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
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Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor. Biomed Eng Lett 2019; 9:387-394. [PMID: 31456898 DOI: 10.1007/s13534-019-00112-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/17/2019] [Accepted: 05/03/2019] [Indexed: 11/27/2022] Open
Abstract
This paper presents a new class of local neighborhood based wavelet feature descriptor (LNWFD) for content based medical image retrieval (CBMIR). To retrieve images effectively from large medical databases is backbone of diagnosis. Existing wavelet transform based medical image retrieval methods suffer from high length feature vector with confined retrieval performance. Triplet half-band filter bank (THFB) enhanced the properties of wavelet filters using three kernels. The influence of THFB has employed in the proposed method. First, triplet half-band filter bank (THFB) is used for single level wavelet decomposition to obtain four sub-bands. Next, the relationship among wavelet coefficients is exploited at each sub-band using 3 × 3 neighborhood window to form LNWFD pattern. The novelty of the proposed descriptor lies in exploring relation between wavelet transform values of pixels rather than intensity values which gives more detail local information in wavelet sub-bands. Thus, proposed feature descriptor is robust against illumination. Manhattan distance is used to compute similarity between query feature vector and feature vector of database. The proposed method is tested for medical image retrieval using OASIS-MRI, NEMA-CT, and Emphysema-CT databases. The average retrieval precisions achieved are 71.45%, 99.51% of OASIS-MRI and NEMA-CT databases for top ten matches considered respectively and 55.51% of Emphysema-CT database for top 50 matches. The superiority in terms of performance of the proposed method is confirmed by the experimental results over the well-known existing descriptors.
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Bielecka M, Obuchowicz R, Korkosz M. The shape language in application to the diagnosis of cervical vertebrae pathology. PLoS One 2018; 13:e0204546. [PMID: 30307944 PMCID: PMC6181296 DOI: 10.1371/journal.pone.0204546] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 09/11/2018] [Indexed: 11/18/2022] Open
Abstract
In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes-the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour.
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Affiliation(s)
- Marzena Bielecka
- Chair of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection AGH University of Science and Technology, Cracow, Poland
- * E-mail:
| | - Rafał Obuchowicz
- Department of Radiology, Jagiellonian University Medical College, Cracow, Poland
| | - Mariusz Korkosz
- Division of Rheumatology, Departement of Internal Medicine and Gerontology, Jagiellonian University Hospital, Cracow, Poland
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Bielecka M. Syntactic-geometric-fuzzy hierarchical classifier of contours with application to analysis of bone contours in X-ray images. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Lan R, Zhou Y. Medical Image Retrieval via Histogram of Compressed Scattering Coefficients. IEEE J Biomed Health Inform 2017; 21:1338-1346. [DOI: 10.1109/jbhi.2016.2623840] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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A medical image retrieval scheme with relevance feedback through a medical social network. SOCIAL NETWORK ANALYSIS AND MINING 2016. [DOI: 10.1007/s13278-016-0362-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Dubey SR, Singh SK, Singh RK. Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval. IEEE J Biomed Health Inform 2016. [DOI: 10.1109/jbhi.2015.2437396] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Chatbri H, Kameyama K, Kwan P. A comparative study using contours and skeletons as shape representations for binary image matching. Pattern Recognit Lett 2016. [DOI: 10.1016/j.patrec.2015.04.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Dubey SR, Singh SK, Singh RK. Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:5892-5903. [PMID: 26513789 DOI: 10.1109/tip.2015.2493446] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.
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Kumar A, Nette F, Klein K, Fulham M, Kim J. A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval. IEEE J Biomed Health Inform 2015; 19:1734-46. [DOI: 10.1109/jbhi.2014.2361318] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Mustapha A, Hussain A, Samad SA, Zulkifley MA, Diyana Wan Zaki WM, Hamid HA. Design and development of a content-based medical image retrieval system for spine vertebrae irregularity. Biomed Eng Online 2015; 14:6. [PMID: 25595511 PMCID: PMC4349791 DOI: 10.1186/1475-925x-14-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 12/18/2014] [Indexed: 11/18/2022] Open
Abstract
Background Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities. Methods In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image. The main tasks in the modelling phase are to improve and enhance the visibility of the x-ray image for better segmentation results using active shape model (ASM). The segmented vertebral fractures are then characterized in the indexing phase using region-based fracture characterization (RB-FC) and contour-based fracture characterization (CB-FC). Upon a query, the characterized features are compared to the query image. Effectiveness of the retrieval phase is determined by its retrieval, thus, we propose an integration of the predictor model based cross validation neural network (PMCVNN) and similarity matching (SM) in this stage. The PMCVNN task is to identify the correct vertebral irregularity class through classification allowing the SM process to be more efficient. Retrieval performance between the proposed and the standard retrieval architectures are then compared using retrieval precision (Pr@M) and average group score (AGS) measures. Results Experimental results show that the new integrated retrieval architecture performs better than those of the standard CBMIR architecture with retrieval results of cervical (AGS > 87%) and lumbar (AGS > 82%) datasets. Conclusions The proposed CBMIR architecture shows encouraging results with high Pr@M accuracy. As a result, images from the same visualization class are returned for further used by the medical personnel.
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Affiliation(s)
| | - Aini Hussain
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Univeristi Kebangsaan Malaysia, Bangi, 43600 Selangor DE, Malaysia.
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Pan H, Li P, Li Q, Han Q, Feng X, Gao L. Brain CT image similarity retrieval method based on uncertain location graph. IEEE J Biomed Health Inform 2014; 18:574-84. [PMID: 24608057 DOI: 10.1109/jbhi.2013.2274798] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.
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Murala S, Wu QMJ. Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval. IEEE J Biomed Health Inform 2014; 18:929-38. [DOI: 10.1109/jbhi.2013.2288522] [Citation(s) in RCA: 159] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Kumar A, Kim J, Wen L, Fulham M, Feng D. A graph-based approach for the retrieval of multi-modality medical images. Med Image Anal 2013; 18:330-42. [PMID: 24378541 DOI: 10.1016/j.media.2013.11.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Revised: 11/25/2013] [Accepted: 11/27/2013] [Indexed: 11/17/2022]
Abstract
In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects.
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Affiliation(s)
- Ashnil Kumar
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, Australia.
| | - Jinman Kim
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, Australia.
| | - Lingfeng Wen
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, Australia; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, Australia.
| | - Michael Fulham
- Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia.
| | - Dagan Feng
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, Australia; Med-X Research Institute, Shanghai Jiao Tong University, China.
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Kumar A, Kim J, Cai W, Fulham M, Feng D. Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J Digit Imaging 2013; 26:1025-39. [PMID: 23846532 PMCID: PMC3824925 DOI: 10.1007/s10278-013-9619-2] [Citation(s) in RCA: 138] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
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-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. 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 criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.
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Affiliation(s)
- Ashnil Kumar
- Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Building J12, Sydney, NSW, 2006, Australia,
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Mori K, Ito T, Miyamoto H, Ozawa M, Wada S, Kumagai Y, Matsumoto J, Naito R, Nakamura S, Kodama H, Kurihara Y. Oral administration of multispecies microbial supplements to sows influences the composition of gut microbiota and fecal organic acids in their post-weaned piglets. J Biosci Bioeng 2011; 112:145-50. [DOI: 10.1016/j.jbiosc.2011.04.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2011] [Revised: 04/01/2011] [Accepted: 04/21/2011] [Indexed: 12/24/2022]
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Influence of age, gender and weight on spinal osteoarthritis in the elderly: An analysis of morphometric changes using X-ray images. Ing Rech Biomed 2010. [DOI: 10.1016/j.irbm.2009.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Using relevance feedback with short-term memory for content-based spine X-ray image retrieval. Neurocomputing 2009. [DOI: 10.1016/j.neucom.2008.12.029] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Hsu W, Antani S, Long LR, Neve L, Thoma GR. SPIRS: a Web-based image retrieval system for large biomedical databases. Int J Med Inform 2009; 78 Suppl 1:S13-24. [PMID: 18996737 PMCID: PMC2693318 DOI: 10.1016/j.ijmedinf.2008.09.006] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2008] [Revised: 08/11/2008] [Accepted: 09/16/2008] [Indexed: 10/21/2022]
Abstract
PURPOSE With the increasing use of images in disease research, education, and clinical medicine, the need for methods that effectively archive, query, and retrieve these images by their content is underscored. This paper describes the implementation of a Web-based retrieval system called SPIRS (Spine Pathology & Image Retrieval System), which permits exploration of a large biomedical database of digitized spine X-ray images and data from a national health survey using a combination of visual and textual queries. METHODS SPIRS is a generalizable framework that consists of four components: a client applet, a gateway, an indexing and retrieval system, and a database of images and associated text data. The prototype system is demonstrated using text and imaging data collected as part of the second U.S. National Health and Nutrition Examination Survey (NHANES II). Users search the image data by providing a sketch of the vertebral outline or selecting an example vertebral image and some relevant text parameters. Pertinent pathology on the image/sketch can be annotated and weighted to indicate importance. RESULTS During the course of development, we explored different algorithms to perform functions such as segmentation, indexing, and retrieval. Each algorithm was tested individually and then implemented as part of SPIRS. To evaluate the overall system, we first tested the system's ability to return similar vertebral shapes from the database given a query shape. Initial evaluations using visual queries only (no text) have shown that the system achieves up to 68% accuracy in finding images in the database that exhibit similar abnormality type and severity. Relevance feedback mechanisms have been shown to increase accuracy by an additional 22% after three iterations. While we primarily demonstrate this system in the context of retrieving vertebral shape, our framework has also been adapted to search a collection of 100,000 uterine cervix images to study the progression of cervical cancer. CONCLUSIONS SPIRS is automated, easily accessible, and integratable with other complementary information retrieval systems. The system supports the ability for users to intuitively query large amounts of imaging data by providing visual examples and text keywords and has beneficial implications in the areas of research, education, and patient care.
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Affiliation(s)
- William Hsu
- Medical Imaging Informatics Group, University of California, Los Angeles, USA
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