Content-based Image Retrieval Applications Research Articles

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1-25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter @k of LMNN (@k>=25) while LiRaM LVQ outperforms LMNN for smaller values of @k. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images.

Image Retrieval using Bit-plane Pixel Distribution

Users needing is to store and index the image data and retrieved the image on feature vector derived by the user. Content Based Image Retrieval (CBIR) is search engine to retrieving the desired image automatically from the large image database having different categories. Retrieving the relevant images from the database by using feature vector is the challenging and important task. It is also need to retrieve the images from variety of the domain that is the application of CBIR that domains are medicine, crime prevention ,Biometrics, architecture, Fashion and publishing. This paper present the method developed to search and retrieve the similar image using bit plane image. Bit plane images are formed by using threshold and using bit plane slicing. Mean, standard deviation and third moment of row and column pixel distribution of bit plane image is used as a feature vector. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. The average precision and average recall of each image category and over all precision and recall is considered for the performance measure.

A Center-Surround Histogram for content-based image retrieval

In this paper, a new type of histogram which incorporates only the visual information surrounding the edges of the image is introduced. The edge extraction operation is performed with the use of a center-surround operator of the Human Visual System. The proposed Center-Surround Histogram (CSH) has two main advantages over the classic histogram. First, it reduces the amount of visual information that needs to be processed and second, it incorporates a degree of spatial information when used in content based image retrieval applications. The method is compared with other contemporary image retrieval methods, including that of another edge color histogram, on two different databases. The comparison shows that the use of CSH exhibits better results in shorter execution times.

An Efficient Hierarchical Clustering Algorithm and its Application for Image Retrieval

Clustering is an efficient and fundamental unsupervised learning algorithm for many vision-based applications. This paper aim at the problems of fast indexing high-dimensional local invariant features of images (e.g. SIFT features) and quick similarity searching of images in a scalable image database by using a hierarchical clustering algorithm. We adopt the hierarchical K-means (HKM) clustering method to build a visual vocabulary tree efficiently on given training data and represent image as a “bag of visual words” which are the leaf nodes of the visual vocabulary tree. For the application of image retrieval, we adopt an usually-used indexing structure called “inverted file” to record the mapping of each visual word to the database images containing that visual word along with the number of times it appears in each image. We propose a weighted voting strategy for the application of content-based image retrieval and achieve desirable performance through experiments.

CONTENT BASED IMAGE RETRIEVAL USING EXACT LEGENDRE MOMENTS AND SUPPORT VECTOR MACHINE

Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).

Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach

Segmentation, where pixels are categorized by tissue types, is essential in medical image processing. This paper proposes a multi-level Fuzzy C-Means method to extract an intracranial from its background and skull. Then, a two-level Otsu multi-thresholding method is applied to segment the intracranial structure into cerebrospinal fluid, brain matters and other homogenous regions. Based on symmetrical properties in the intracranial structures, the left-half and right-half segmented intracranial regions are quantitatively compared with respect to the intracranial midline. The segmented regions are found to be very useful in providing information regarding normal and abnormal structures in the intracranial because any asymmetry that is detected would indicate a high probability of abnormalities. Additionally, pixel intensity information such as standard deviation and the maximum value of the pixels of the segmented regions are used to distinguish abnormalities such as bleeding and calcification from normal cases. This experimental work uses a medical image database consisting of 519 normal and 201 abnormal serial computed tomography (CT) brain images from 31 patients. The proposed multi-level segmentation approach proved to effectively isolate important homogenous regions in CT brain images. The extracted features of the regions would provide a strong basis for the application of content-based medical image retrieval (CMBIR).

Workflow management of content-based image retrieval for CAD support in PACS environments based on IHE

Content-based image retrieval (CBIR) bears great potential for computer-aided diagnosis (CAD). However, current CBIR systems are not able to integrate with clinical workflow and PACS generally. One essential factor in this setting is scheduling. Applied and proved with modalities and the acquisition of images for a long time, we now establish scheduling with CBIR. Our workflow is based on the IHE integration profile 'Post-Processing Workflow' (PPW) and the use of a DICOM work list. We configured dcm4chee PACS and its including IHE actors for the application of CBIR. In order to achieve a convenient interface for integrating arbitrary CBIR systems, we realized an adapter between the CBIR system and PACS. Our system architecture constitutes modular components communicating over standard protocols. The proposed workflow management system offers the possibility to embed CBIR conveniently into PACS environments. We achieve a chain of references that fills the information gap between acquisition and post-processing. Our approach takes into account the tight and solid organization of scheduled and performed tasks in clinical settings.

10.1007/s11493-008-1004-5

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision-recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications.

Bridging the Feature Gaps for Retrieval of Multi-Dimensional Images

Content-based image retrieval (CBIR) refers to the use of visual features for images retrieval, and has become an attractive approach to managing biomedical image achieves. However, existing CBIR systems are typically designed for 2D single-modality imaging, and are restricted when multi-dimensional images are considered. With the advances in imaging scanners, image complexity and magnitude have rapidly expanded in both the temporal (time) and spatial (space) dimensions, i.e., dynamic PET provides physiological functions of the human body acquired in 3D volumes over multiple time sequences, and dual-modality imaging scanners that sequentially acquires co-aligned functional (PET) and anatomical (CT) images. This manuscript summarizes research in CBIR of multi-dimensional biomedical images with focuses on the feature extraction and retrieval techniques that utilize the information available in the image’s multidimensional data spaces. Applications of multi-dimensional CBIR will be exemplified with our ongoing studies with 4D dynamic PET and dual-modal PET/CT images.

A generalized family of fixed-radius distribution-based distance measures for content-based fMRI image retrieval

We present a family of distance measures for comparing activation patterns captured in fMRI images. We model an fMRI image as a spatial object with varying density, and measure the distance between two fMRI images using a novel fixed-radius, distribution-based Earth Mover’s Distance that is computable in polynomial time. We also present two simplified formulations for the distance computation whose complexity is better than linear programming. The algorithms are robust in the presence of noise, and by varying the radius of the distance measures, can tolerate different degrees of within-class deformation. Empirical evaluation of the algorithms on a dataset of 430 fMRI images in a content-based image retrieval application demonstrates the power and robustness of the distance measures.

Ontology of Gaps in Content-Based Image Retrieval

Content-based image retrieval (CBIR) is a promising technology to enrich the core functionality of picture archiving and communication systems (PACS). CBIR has a potential for making a strong impact in diagnostics, research, and education. Research as reported in the scientific literature, however, has not made significant inroads as medical CBIR applications incorporated into routine clinical medicine or medical research. The cause is often attributed (without supporting analysis) to the inability of these applications in overcoming the "semantic gap." The semantic gap divides the high-level scene understanding and interpretation available with human cognitive capabilities from the low-level pixel analysis of computers, based on mathematical processing and artificial intelligence methods. In this paper, we suggest a more systematic and comprehensive view of the concept of "gaps" in medical CBIR research. In particular, we define an ontology of 14 gaps that addresses the image content and features, as well as system performance and usability. In addition to these gaps, we identify seven system characteristics that impact CBIR applicability and performance. The framework we have created can be used a posteriori to compare medical CBIR systems and approaches for specific biomedical image domains and goals and a priori during the design phase of a medical CBIR application, as the systematic analysis of gaps provides detailed insight in system comparison and helps to direct future research.

Performance study of an improved Legendre Moment Descriptor as region-based shape descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision-recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications.

Extended Query Refinement for Medical Image Retrieval

The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of man-machine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nested loops for interaction provide a maximum of flexibility within a minimum of complexity, as the entire data flow is still controlled within a single Web page. Our approach is implemented to support various modalities, orientations, and body regions using global features that model gray scale, texture, structure, and global shape characteristics. The resulting extended query refinement has a significant impact for medical CBIR applications.

Integrated color, texture and shape information for content-based image retrieval

Feature extraction and the use of the features as query terms are crucial problems in content-based image retrieval (CBIR) systems. The main focus in this paper is on integrated color, texture and shape extraction methods for CBIR. We have developed original CBIR methodology that uses Gabor filtration for determining the number of regions of interest (ROIs), in which fast and effective feature extraction is performed. In the ROIs extracted, texture features based on thresholded Gabor features, color features based on histograms, color moments in YUV space, and shape features based on Zernike moments are then calculated. The features presented proved to be efficient in determining similarity between images. Our system was tested on postage stamp images and Corel photo libraries and can be used in CBIR applications such as postal services.

Image retrieval: Benchmarking visual information indexing and retrieval systems

Image retrieval: Benchmarking visual information indexing and retrieval systems

Invariant salient regions based image retrieval under viewpoint and illumination variations

In this paper, we present a novel image retrieval technique based on salient regions that are invariant under viewpoint and illumination variations. The salient regions are detected according to local entropy and scale selection. The detected regions have very high repeatability under various viewpoint and illumination changes. We apply the invariant region detector on content-based image retrieval applications. The detected regions are the most informative ones, hence are potentially more effective for image indexing and retrieval. Generalized colour moment invariants are used as the invariant descriptor for characterizing the selected salient regions under geometric and photometric changes. Two datasets are used for experiments: one for evaluating the invariance of the algorithm under geometric and photometric transformations; the other for testing the algorithm on object category retrieval. The experimental results show that our proposed region detector is very efficient and effective on retrieving a variety of cluttered images with partial occlusion.

Automatic texture segmentation for content-based image retrieval application

In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images, real scene images as well as our main source of images, the museum images of various kinds. An extension to the automatic texture segmentation, a texture identifier is also introduced for integration into a retrieval system, providing an excellent approach to content-based image retrieval using texture features.

Automatic categorization of medical images for content-based retrieval and data mining

Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.

Expert system for color image retrieval

Recently, as Web and various databases contain a large number of images, content-based image retrieval (CBIR) applications are greatly needed. This paper proposes a new image retrieval system using color-spatial information from those applications. First, this paper suggests two kinds of indexing keys to prune away irrelevant images to a given query image: major colors' set (MCS) signature related with color information and distribution block signature (DBS) related with spatial information. After successively applying these filters to a large database, we get only small amount of high potential candidates that are somewhat similar to a query image. Then we make use of the quad modeling (QM) method to set the initial weights of two-dimensional cell in a query image according to each major color. Finally, we retrieve more similar images from the database by comparing a query image with candidate images through a similarity measuring function associated with the weights. In that procedure, we use a new relevance feedback mechanism. This feedback enhances the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed system is not only efficient but also effective.

Developing an object‐oriented framework for content‐based image retrieval

AbstractContent‐based image retrieval (CBIR) is a process of retrieving images from an image database by exploiting the content of the images (typically the querying of an image). CBIR avoids many problems associated with traditional ways of retrieving images by keywords. Thus, a growing interest in the area of CBIR has been established in recent years. In this paper, a novel object‐oriented framework (CBIRFrame) is built for CBIR applications development. We discuss the motivations for CBIRFrame before discussing its design in detail. Two applications of CBIRFrame are also briefly discussed to show the effectiveness of applying CBIRFrame to real applications. Finally, we outline the possible uses of the design of CBIRFrame for other types of domains, such as content‐based retrieval of video clips. Copyright © 2003 John Wiley & Sons, Ltd.