Top PDF PERFORMANCE EVALUATION OF CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES

PERFORMANCE EVALUATION OF CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES

PERFORMANCE EVALUATION OF CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES

Quellec et al [8] proposed a CBIR method for diagnosis in medical fields. In this, images are indexed in a generically, without extracting domain-specific features: a signature is built into each image from wavelet transform. These signatures characterize wavelet coefficient distribution in each decomposition subband. A distance measure compares two image signatures and retrieves most similar images from the database when a physician submits a query image. To retrieve relevant images from a medical database, signatures and distance measure should be related to medical image interpretation. Subsequently the system requires much freedom to tune it to any pathology with image modality being introduced. The scheme proposed using a custom decomposition scheme to adapt the wavelet basis with lifting scheme framework. Weights are introduced between subbands. All parameters are tuned by an optimization procedure, using database medical image grading to define performance measures. System assessment is through two medical image databases: one for diabetic retinopathy follow up and another for mammography screening, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% are achieved for the three databases, when the system returned five images.
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Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

the feasibility of the proposed CBIR system, rectangular ROIs (Figure 2) are used in the current study instead of tumors contours for feature extraction; this method demands a more subtle extraction approach to integrate the spatial information into the BoVW-based image feature. Several other approaches have addressed this problem for object recognition tasks [25–28]. The spatial pyramid method [25], for example, is a representative of these methods. The spatial pyramid method symmetrically partitions the image into uniform cells at a series of resolution levels (i.e., 161, 262, and 464) and then combines all of the BoVW histograms in each cell to incorporate global and local information. With the help of spatial layout information to improve the discriminative power, the spatial pyramid method outperforms the basic BoVW model and has thus been employed in several studies [29,30]. However, compared with the symmet- rical partitioning in the spatial pyramid method, the partition can be learned to make the feature more discriminative [28] when the contents of the images from a specific category are distributed with intrinsic regularity. Brain tumor T1-weghted CE-MR Images show that the layouts of the tumor, edema, and surrounding normal tissue have strong correlations [6]. For instance, menin- giomas and pituitary tumors are homogeneous lesions [Figures 2(A) and 2(B)]; these two tumor types are usually not associated with edema. Meningiomas are usually adjacent to the skull, gray matter, and cerebrospinal fluid. Pituitary tumors are adjacent to the sphenoidal sinus, internal carotid arteries, and optic chiasma. Figure 1. Four examples of brain tumors in T1-weighted CE-MR Images. The tumors are indicated by the yellow arrows in each image. (A) and (B) Gliomas in different subjects having dissimilar appearances. (C) A meningioma and (D) a pituitary tumor from different subjects showing a similar appearance.
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Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words

Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words

Abstract —Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query, SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The experimental results reveal that this system performs well on two different standard datasets.
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REPOSITORIO INSTITUCIONAL DA UFOP: Object-based image retrieval using local feature extraction and relevance feedback.

REPOSITORIO INSTITUCIONAL DA UFOP: Object-based image retrieval using local feature extraction and relevance feedback.

The increasing production of visual information (pictures and videos) in recent years has intensified the demand for multimedia information systems that are able to efficiently store and retrieve files of this nature in large databases [16]. According to [18,9], pictures have to be seen and searched as pictures: by objects, by style, by purpose. In this context, content based image retrieval (CBIR) methods, which use search keys that are extracted automatically from the visual content of images have been developed to improve the performance of visual information management systems [3,20]. This work presents an approach that belongs to this group of methods, which uses local feature extraction, a relevance feedback mechanism and a clustering algorithm to perform object-based image retrieval.
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Content based image retrieval based on eye physiological structure and relevance feedback

Content based image retrieval based on eye physiological structure and relevance feedback

Weight at beginning obtains feature vectors by using the attention and Gaussian filter. In follow, by use of relevance feedback and fuzzy rules are intended to update the similarity measure. Considering that our proposed method have used 20 to 100 image returned by the system. So according to the number of relevant images returned by the system and consider its membership value Gaussian filter obtains value of membership that stage than five fuzzy sets "very low, low, medium, high, very high" .For instance returned image by sets fuzzy system are as follows
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A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

Content Based Image Retrieval (CBIR) has become an important area of research with the ever increasing demand and use of digital images in various fields such as medicine, engineering, sciences, digital photography etc. Unlike the traditional method of text-based image retrieval in which the image search is based on textual description associated with the images, CBIR systems retrieve images based on the content of the image such as colour, texture, shape or any other information that can be automatically extracted from the image itself and using it as a criterion to retrieve content related images from the database. The retrieved images are then ranked according to the relevance between the query image and images in the database in proportion to a similarity measure calculated from the features [1][2][3].
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MammoSys: A content-based image retrieval system using breast density patterns

MammoSys: A content-based image retrieval system using breast density patterns

Table 1 lists the execution time of the MammoSys system, comparing the breast tissue characterization using 2DPCA, PCA, and SVD for different values of d. 2DPCA took more time to execute the retrieval, as expected, since each principal component is a vector, while for PCA and SVD each princi- pal component is a scalar. Also, with d = 1, . . . , 4, 2DPCA was slower than for others values. Machine learning algorithms like SVM are influenced by data, i.e., the number of features may degrade computational performance. If the number of features is too small or not significant, the support vectors may not be able to correctly separate the data and indicate the rel- evance of the images, therefore taking more time in this task. Also, there was no significant difference between the values of
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A Meta-Heuristic Optimization Approach for Content Based Image Retrieval using Relevance Feedback Method

A Meta-Heuristic Optimization Approach for Content Based Image Retrieval using Relevance Feedback Method

In our experiments, we used the Corel database covering a wide range of semantic categories from natural scenes to artificial objects. The dataset is partitioned into 10 categories, including Butterfly, buildings, hills, flowers, earth, sky, trees, boats, birds, statue, horses, and elephants, etc., and each category is represented by 250 images, for a total of 2500 images. All the experiments were implemented in Matlab, running on a personal computer with Intel Dual Core 3GHZ processor and 4 GB RAM. To analyze the effectiveness of our proposed approach precision, recall, F- Measure, true positive and false positive are used to measure the related experimental evaluations.
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A Novel Algorithm for Region-Based Image Retrieval Framework

A Novel Algorithm for Region-Based Image Retrieval Framework

In the last decade the number of medical images especially digital images is increasing which is used for diagnosing and therapies. With the wide growth of medical images database, the statistical analysis of medical images is becoming a big challenge. When a physician is studying a case he is interested in more similar cases and the similarity measures usually involves a medical background. The benefit of archiving medical images can be gained only when a good query tool is adapted to allow users to browse the medical images. Content-based image retrieval faces few problems when it is applied on medical images. Two of the major difficulties are:
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Dual-force ISOMAP: a new relevance feedback method for medical image retrieval.

Dual-force ISOMAP: a new relevance feedback method for medical image retrieval.

us take medical images obtained from IRMA medical image dataset [4] as an example. The IRMA medical image dataset is a widely used test bed for performance evaluation of CBMIR [5–8]. The new version of IRMA dataset [4] contains 12,677 fully annotated gray value radiographs in a training set. These images are categorized into 193 classes according to a mono-hierarchical multi-axial classification standard called the IRMA coding system [9]. The system classifies a medical image from four orthogonal axes: imaging modality, body orientation, body region examined and biological system examined. Figure 1 and Figure 2 illustrate the scenario of semantic gap. As shown in Figure 1, two chest radiographs have a similar visual appearance, but their semantic meanings are different. The IRMA code [9] of the left image is ‘‘1123-127-500-000’’, while the IRMA code of the right image is ‘‘1123-110-500-003’’. By contrast, though their visual appearance is different, the two mammograms shown in Figure 2 have the same IRMA code ‘‘1124-310-610-625’’.
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Enhancement Techniques for Local Content Preservation and Contrast Improvement in Images

Enhancement Techniques for Local Content Preservation and Contrast Improvement in Images

There are several images that do not have uniform brightness which pose a challenging problem for image enhancement systems. As histogram equalization has been successfully used to correct for uniform brightness problems, a histogram equalization method that utilizes human visual system based thresholding(human vision thresholding) as well as logarithmic processing techniques were introduced later . But these methods are not good for preserving the local content of the image which is a major factor for various images like medical and aerial images. Therefore new method is proposed here. This method is referred as “Human vision thresholding with enhancement technique for dark blurred images for local content preservation”. It uses human vision thresholding together with an existing enhancement method for dark blurred images. Furthermore a comparative study with another method for local content preservation is done which is further extended to make it suitable for contrast improvement . Experimental results shows that the proposed methods outperforms the former existing methods in preserving the local content for standard images ,medical and aerial images .
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Content Based Image Retrival of MRI of Human Brain

Content Based Image Retrival of MRI of Human Brain

With the rapid development of technology, the traditional information retrieval techniques based on keywords are not sufficient purposefully Content-based image retrieval has been a vigorous area of research for at least the last two Decades. Content Based Image Retrieval (CBIR) technologies provide a method to find images in large databases by using unique descriptors from a trained image. It plays a central role in the application areas such as multimedia database systems in recent years. The work focused on using low-level features like color, texture, shape and spatial layout for image representation (Fuhuri et al,2003).
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Feature extraction in content based video retrieval

Feature extraction in content based video retrieval

Texture is that property of surfaces that describes visual patterns. Co occurrence Matrix method is used for Texture-Based Features extraction. Texture represented by pixels gives relative brightness of consecutive pixels and finds the degree of contrast, regularity, coarseness and directionality which classifies textures as 'smooth', 'rough' etc. Texture is a visual pattern where there are a large number of visible elements densely and evenly arranged. A texture element is a uniform intensity region of simple shape which is repeated. We divide the image into 55 blocks and compute texture features using Gabor-wavelet filters in each block. The merit of texture-based features is that they can be effectively applied to applications in which texture information is salient in videos. However, these features are unavailable in non texture video images. Depending on the texture on the foreground and background regions in a given video, we will get the trajectories from both parts from input video and from database videos.
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Contextual Region of Interest Based Medical Image Compression using Contextual Listless SPIHT Algorithm for Brain Images

Contextual Region of Interest Based Medical Image Compression using Contextual Listless SPIHT Algorithm for Brain Images

In this work, we have proposed Contextual Listless SPIHT (CLSPIHT) which uses region of interest concept. Our main idea is to separate the contextual region of interest which contains the most important information for diagnosis and the back ground image which consists of patient information. Here, CROI region is encoded separately and the BG regions are encoded separately using Contextual Listless SPIHT. Finally, the two extracted regions are merged together to get the reconstructed image. The results obtained in this method are listed in Table I and Table II and it clearly show the better performance in terms of PSNR, CR, MSE and CoC for different bit rates. The performance parameters bpp and the PSNR comparative results of different methods are compared and our method show the improved performance. From the comparison with the traditional methods such as JPEG2000 and SPIHT, our method provides better compression performance in terms of PSNR and CoC as well as good visual quality of reconstructed image. So, we conclude that our proposed CLSPIHT method is a good selection for minimizing the storage cost and the transmission time of medical images.
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Augmentation of Colour Averaging Based Image Retrieval Techniques using Even part of Images and Amalgamation of feature vectors

Augmentation of Colour Averaging Based Image Retrieval Techniques using Even part of Images and Amalgamation of feature vectors

In spite of many CBIR methods being proposed, there is always a scope of better image retrieval techniques. The paper has presented augmentation of colour averaging based image retrieval techniques using even part of image. Here in all 6 different image retrieval methods based on original image and original with even image are tested on the image database of 1000 images spread across 11 categories. The average precision and recall values have proved that the original with even image gives better performance than other CBIR methods at the cost of increased feature vector size. In all three colour averaging methods are discussed, where row, column and forward diagonal mean (RCFDM) proves to be the best image retrieval method. The image retrieval based on RCFDM with original+even image outperforms the other CBIR techniques.
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Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks

Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks

Abstract— This paper introduces a new CBIR system based on two different approaches in order to achieve the retrieval efficiency and accuracy. Color and texture information is extracted and used in this work to form the feature vector. To do the texture feature extraction this system uses DCT and DCT Wavelet transform to generate the feature vectors of the query and database images. Color information extraction process includes separation of image into R, G and B planes. Further each plane is divided into 4 blocks and for each block row mean vectors are calculated. DCT and DCT wavelet is applied over row mean vector of each block separately and 4 sets of DCT and DCT wavelet coefficients are obtained respectively. Out of these few coefficients are selected from each block and arranged in consecutive order to form the feature vector of the image. Variable size feature vectors are formed by changing the no of coefficients selected from each row vector. Total 18 different sets are obtained by changing the no of coefficients selected from each block. These two different feature databases obtained using DCT and DCT wavelet are then tested using 100 query images from 10 different categories. Euclidean distance is used as similarity measure to compare the image features. Euclidean distance calculated is sorted into ascending order and cluster of first 100 images is selected to count the images which are relevant to the query image. Results are further refined using second level thresholding which uses three criteria which can be applied to first level results. Results obtained are showing the better performance by DCT wavelet as compare to DCT transform.
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Performance evaluation of Different Annotation retrieval methods

Performance evaluation of Different Annotation retrieval methods

The dataset used are from Lung Image Database Consortium, it is a publicly available, well characterized repository of Lung CT images, with annotations of more than one experienced radiologists done by consensus between them. Further, help from Jawaharlal Nehru Post Graduate Institute of Medical Education and Research, Chandigarh is taken. In this research work, 8 scan set, each one consists on an average of 190 slices were taken under consideration. The images were acquired with a 512*512 matrix and quantized with 16 bits. These images were transferred into the Digital Imaging and Communications in Medicine (DICOM) format at which, the Hounsfield units for attenuation were translated into brightness values. The databases have been since then available online to the public, and have been used by many researchers.
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A Novel Content-Based Image Retrieval Technique Using Tree Matching

A Novel Content-Based Image Retrieval Technique Using Tree Matching

CBIR system extracts important visual features of an image, then generates a vector based on the image features, called feature vector and calculates the similarity of feature vector, extracted from query image, and other images stored in the database. Finally, it presents and ranks a sequence of similar images to the query one, based on a similarity measurement. There are two groups of CBIR system: histogram and region based methods. The histogram-based methods extract global features, such as color, texture and shape while the region-based CBIR systems segment images into some specific regions and retrieves images based on the similarity between them.
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Global Descriptor Attributes Based Content Based Image Retrieval of Query Images

Global Descriptor Attributes Based Content Based Image Retrieval of Query Images

However, these methods have their own drawback that must be solved to improve the performance of CBIR[4]. Basic single Gaussian assumption which proposed by MDA and BDA usually doesn‟t hold, since the few training samples are always scattered in the high dimensional feature space, and their effectiveness will be suffer. Moreover, single Gaussian distribution means all positive samples should be similar with similar view angle and similar illumination, which are not the case for CBIR. To overcome the problem of single Gaussian distribution assumption, KBDA had been introduced. But, this kernel based method has two major drawbacks which is regularization approach is often unstable and it is rely on parameter tuning. Then, NDA had been proposed to solve the problem in MDA, BDA and also KBDA. This approach can only barely match the accuracy performance of KBDA. As a conclusion, many feature selection methods can not satisfy the requirements in CBIR even though there are many method has been apply in content-based image retrieval[5].
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Text and Content Based Image Retrieval Via Locality Sensitive Hashing

Text and Content Based Image Retrieval Via Locality Sensitive Hashing

Abstract—We present a scalable image retrieval system based jointly on text annotations and visual content. Previous ap- proaches in content based image retrieval often suffer from the semantic gap problem and long retrieving time. The solution that we propose aims at resolving these two issues by indexing and retrieving images using both their text descriptions and visual content, such as features in colour, texture and shape. A query in this system consists of keywords, a sample image and relevant parameters. The retrieving algorithm first selects a subset of images from the whole collection according to a comparison between the keywords and the text descriptions. Visual features extracted from the sample image are then compared with the extracted features of the images in the subset to select the closest. Because the features are represented by high-dimensional vectors, locality sensitive hashing is applied to the visual comparison to speedup the process. Experiments were performed on a collection of 1514 images. The timing results showed the potential of this solution to be scaled up to handle large image collections.
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