Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Most content-based image retrievals (CBIR) use color as image features. However, image retrieval using color features often gives disappointing results because in many cases, images with similar colors do not have similar content. Color methods incorporating spatial information have been proposed to solve this problem, however, these methods often result in very high dimensions of features which drastically slow down the retrieval speed. In this paper, a method combining both color and texture features of image is proposed to improve the retrieval performance. Given a query, images in the database are firstly ranked using color features. Then, the top ranked images are re-ranked according to their texture features. Results show the second process improves retrieval performance significantly. <s> BIB001 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Aim to currently content-based image retrieval method having high computational complexity and low retrieval accuracy problem, this paper proposes a content-based image retrieval method based on color and texture features. As its color features, color moments of the Hue, Saturation and Value (HSV) component images in HSV color space are used. As its texture features, Gabor texture descriptors are adopted. Users assign the weights to each feature respectively and calculate the similarity with combined features of color and texture according to normalized Euclidean distance. Experiment results show that the proposed method has higher retrieval accuracy than conventional methods using color and texture features even though its feature vector dimension results in a lower rate than the conventional method. <s> BIB002 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> In this paper, we take advantage of single-valued functions that evaluate rankings to develop a family of feature selection methods based on the genetic algorithm approach, tailored to improve the accuracy of content-based image retrieval systems. Experiments on three image datasets, comprising images of breast and lung nodules, showed that developing functions to evaluate the ranking quality allows improving retrieval performance. This approach produces significantly better results than those of other fitness function approaches, such as the traditional wrapper and than filter feature selection algorithms. <s> BIB003 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> A content-based mammogram retrieval system can support usual comparisons made on images by physicians, answering similarity queries over images stored in the database. The importance of searching for similar mammograms lies in the fact that physicians usually try to recall similar cases by seeking images that are pathologically similar to a given image. This paper presents a content-based mammogram retrieval system, which employs a query example to search for similar mammograms in the database. In this system the mammographic lesions are interpreted based on their medical characteristics specified in the Breast Imaging Reporting and Data System (BI-RADS) standards. A hierarchical similarity measurement scheme based on a distance weighting function is proposed to model user's perception and maximizes the effectiveness of each feature in a mammographic descriptor. A machine learning approach based on support vector machines and user's relevance feedback is also proposed to analyze the user's information need in order to retrieve target images more accurately. Experimental results demonstrate that the proposed machine learning approach with Radial Basis Function (RBF) kernel function achieves the best performance among all tested ones. Furthermore, the results also show that the proposed learning approach can improve retrieval performance when applied to retrieve mammograms with similar mass and calcification lesions, respectively. <s> BIB004 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> This paper presents a method to extract color and texture features of an image quickly for content-based image retrieval (CBIR). First, HSV color space is quantified rationally. Color histogram and texture features based on a co-occurrence matrix are extracted to form feature vectors. Then the characteristics of the global color histogram, local color histogram and texture features are compared and analyzed for CBIR. Based on these works, a CBIR system is designed using color and texture fused features by constructing weights of feature vectors. The relevant retrieval experiments show that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results. <s> BIB005 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background. <s> BIB006 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> This paper describes ontology-based annotation of mammography and a Case-based Retrieval approach for breast masses from digital mammography archive. We first present our Mammography Annotation Ontology focusing on its main concepts and relationships, as well as the annotation tool. Then, we propose a model for similarity calculation between breast masses based on their high, mid and low-level features. We use Semantic Query-enhanced Web Rule Language (SQWRL) to process retrieval of similar masses from annotated mammography collection in OWL. We give both retrieving process and results we obtained from experimentations, in detail. <s> BIB007 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Based Image Retrieval (CBIR) systems help users to retrieve relevant images based on their contents such as color and texture. In this paper, a new approach is proposed in which color histogram, color moment and Gabor texture descriptors are integrated. The color histogram has the advantages of rotation and translation invariance. The HSV (16, 4, 4) quantization scheme has been adopted for color histogram and an image is represented by a vector of 256- dimension. The color histogram has the disadvantages of lack of spatial information and to improve the discriminating power of color indexing techniques, a minimal amount of spatial information is encoded in the color index by dividing the image horizontally into three equal non-overlapping regions and extracts the three moments (mean, variance and skewness) from each region, for all the color channels. Thus, for a HSV color space, 27 floating point numbers per image are used for indexing. As its texture feature, Gabor texture descriptors are adopted. Weights are assigned to each feature respectively and calculate the similarity with combined features of color histogram, color moment and Gabor texture using Histogram intersection distance and Canberra distance as similarity measures. Experimental results show that the proposed method has higher retrieval accuracy in terms of precision than other conventional methods combining color histogram, color moment and Gabor texture based on global features approach. <s> BIB008 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> The paper presents an efficient Content Based Image Retrieval (CBIR) system using color and texture. In proposed system, two different feature extraction techniques are employed. A universal content based image retrieval system uses color, texture and shape based feature extraction techniques for better matched images from the database. In proposed CBIR system, color and texture features are used. The texture features were extracted from the query image by applying block wise Discrete Cosine Transforms (DCT) on the entire image and from the retrieved images the color features were extracted by using moments of colors (Mean, Deviation and Skewness) theory. The proposed system has used Corel database of 1000 images. The feature vectors of the query image will then be compared with feature vectors of the database to obtain similar images. Individual and combined vectors using color and texture features were computed and the combined feature vector results were comparatively better. The proposed system provides an efficiency of 60%. <s> BIB009 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Health-related information, much of it consisting of images, is being predominantly accessed online by diverse groups of users ranging from medical professionals and researchers to students and the general public. This paper argues that broad-usage medical image retrieval is best approached as a sub-domain of generic image search. We discuss how search over a diverse corpus of biomedical and healthcare related images can benefit from a modern content-based image retrieval (CBIR) system based upon general photographic content classification techniques. The system features a flexible query language based upon a generic image concept ontology which can utilise both metadata (where available) and automatically extracted image content descriptors. Furthermore, the system supports both text-based querying as well as similarity-based searching and is thus well suited to iterative refinement of initial search results without the need for specialist knowledge of relevant keywords. <s> BIB010 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> In this paper we propose an approach for a feature combination helping to distinguish searched images from databases by retrieving relevant images. The retrieval effectiveness of 11 well known image features, commonly used in Content Based Image Retrieval (CBIR) systems, is investigated. We suggest a combined features approach including features’ performance comparison of 57 various medical image categories from IRMA Database. The most informative 3 features, adaptive to image categories, are defined. Based on experiments and image similarity accuracy analysis we suggest a set of 3 low level features Color Layout, Edge Histogram and DCT Coefficients. The developed approach achieves better similar images retrieval results for more image classes. The results show an accuracy improvement of 14.49% on Mean Average Precision (MAP). The comparison is done to the same type performance measure of the best individual feature in different medical image categories. <s> BIB011 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Automatic image annotation is the process of assigning meaningful words to an image taking into account its content. This process is of great interest as it allows indexing, retrieving, and understanding of large collections of image data. This paper presents a system used in the medical domain for three distinct tasks: image annotation, semantic based image retrieval and content based image retrieval. An original image segmentation algorithm based on a hexagonal structure was used to perform the segmentation of medical images. Image's regions are described using a vocabulary of blobs generated from image features using the K-means clustering algorithm. The annotation and semantic based retrieval task is evaluated for two annotation models: Cross Media Relevance Model and Continuous-space Relevance Model. Semantic based image retrieval is performed using the methods provided by the annotation models. The ontology used by the annotation process was created in an original manner starting from the information content provided by the Medical Subject Headings (MeSH). The experiments were made using a database containing color images retrieved from medical domain using an endoscope and related to digestive diseases. <s> BIB012 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Color has been extensively used in the process of image retrieval. The dominant color descriptor (DCD) that was proposed by MPEG-7 is a famous case in point. It is based on compactly describing the prominent colors of an image or a region. However, this technique suffers from some shortcomings; especially with respect to object-based image retrieval. In this paper, a new semantic feature extracted from dominant colors (weight for each DC) is proposed. The newly proposed technique helps reduce the effect of image background on image matching decision where an object's colors receive much more focus. In addition, a modification to DC-based similarity measure is also proposed. Experimental results demonstrate that the proposed descriptor with the similarity measure modification performs better than the existing descriptor in content-based image retrieval application. The proposed descriptor considers as step forward to the object-based image retrieval. <s> BIB013 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Nowadays, the huge volume of medical images represents an enormous challenge towards health-care organizations, as it is often hard for clinicians and researchers to manage, access, and share the image database easily. Content-based medical image retrieval (CBMIR) techniques are employed to facilitate the above process. It is known that a few concrete factors, including visual attributes extracted from images, measures encoding the similarity between images, user interaction, etc. play important roles in determining the retrieval performance. This paper concentrates on the similarity learning problem of CBMIR. A novel similarity learning paradigm is proposed via relative comparison, and a large database composed of 5,000 images is utilized to evaluate the retrieval performance. Extensive experimental results and comprehensive statistical analysis demonstrate the superiority of adopting the newly introduced learning paradigm, compared with several conventional supervised and semi-supervised similarity learning methods, in the presented CBMIR application. <s> BIB014 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Previous Works in CBIR <s> Recent advances in the field of image processing have revealed that ::: the level of noise in mammogram images highly affect the images quality and ::: classification performance of the classifiers. Whilst, numerous data mining ::: techniques have been developed to achieve high efficiency and effectiveness for ::: computer aided diagnosis systems. However, fuzzy soft set theory has been ::: merely experimented for medical images. Thus, this study proposed a classifier ::: based on fuzzy soft set with embedding wavelet de-noising filters. Therefore, the ::: proposed methodology involved five steps namely: MIAS dataset, wavelet ::: de-noising filters hard and soft threshold, region of interest identification, feature ::: extraction and classification. Therefore, the feasibility of fuzzy soft set for ::: classification of mammograms images has been scrutinized. Experimental ::: results show that proposed classifier FussCyier provides the classification performance ::: with Daub3 (Level 1) with accuracy 75.64% (hard threshold), precision ::: 46.11%, recall 84.67%, F-Micro 60%. Thus, the results provide an ::: alternative technique to categorize mammogram images. <s> BIB015
This section examines current practices and prospects of CBIR systems. The emphasis is placed on the summarization of major advanced CBIR approaches used for improving information retrieval. A considerable amount of literature has been published on CBIR systems, while looking into large and growing body of literature; it appears that CBIR systems techniques have been proven to be successful for information retrieval tasks. Town BIB010 described a system called the "Immense Picture Search" that is based on automated analysis and recognition of image content. This system provided two searching modalities which are (1) exploratory search where users could use keyword or a more complex theory and (2) similarity-based refinement where user could find the similar images based on the returned search result. The proposed system is highly suitable for medical area due to the diverse corpus of medical images. The Gray Level Neighbors Matrix (GLNM) was used to extract texture features for the retrieval of mammograms from the MIAS database. The performance of GLNM was compared to the Gabor, CDF 9/7 and Db4 wavelets, GLAM and GLCM based texture extraction methods. The Precision Recall (PR) graphs showed that the retrieval performance of the proposed method is much better than the competing methods. As for the future works, the proposed GLCM approach may be applied for texture feature extraction problems involving other databases, for example diabetic retinopathy and bloats. Popova & Neshov BIB011 attempted to improve the retrieval effectiveness and accuracy of image search in huge databases. Their works recommended two sets of different feature combinations that perform well for medical image categories, IRMA. The top three features used in the research were Colour Layout, Edge Histogram and DCT Coefficients, which were all combined for a higher image re-trivial ranking than others that were based on individual features. The results returned a 14.49% improvement of the retrieval MAP (50.68% for combined features Set). In the future, the research is set to investigate new features and apply appropriate weights for the particular features used in the combination. In handling noisy image in Relevance Feedback CBIR systems, Huang et al., (2013) proposed a two-step strategy. The first step was to apply a noisy elimination algorithm. The second step was to adopt Fuzzy Support Vector Machine (FSVM) to re-rank the images. Noisy-Smoothing Relevance Feedback (NS-RF) was reported to yield better retrieval performance as compared to the Support Vector Machine-Noisy-Smoothing-Relevance Feedback (SVM-NS-RF). The justification was that NS-RF can further handle the noisy images by considering different relevant images with different relevance probabilities in the image re-ranking procedure, whereas SVM-NS-RF treated the retained relevant image equally. In addition, the research noted that the effect of noisy elimination algorithm in NS-RF is sensitive to the slack factor in the proposed formula. Hence, it is proposed to conduct further investigation on the value in order to improve the noisy elimination algorithm in the future. Burdescu et al. BIB012 include Cross Media Relevance Model (CMRM) and Continuous Space Relevance Model (CRM) to annotate the medical images with a segmentation algorithm based on hexagonal structure. The research resulted in an ontology created using Medical Subject Heading (MesH). Their results showed that the CRM model produced better results as compared to CMRM for image annotation and semantic-based image retrieval. Annotation of mammograms has also been explored using the mammography. For Annotation Ontology, a model was proposed for similarity calculation between breast masses based on their high, mid and low-level features. They also used the Semantic Query-enhanced Web Rule Language (SQWRL) to retrieve similar masses from annotated mammography collection in OWL. The proposed approach was tested with DEMS, which is a fullyannotated digital mammography dataset. The mid-level feature also helped to effectively rank the result set. Future work was set to include more mid-level features as well as to integrate the low-level features into reasoning phase BIB007 . Da Silva et al., BIB003 conducted three experiments using mammograms taken from the University of South Carolina and University of Sao Paulo. This technique of feature selection enhanced similarity search accuracy and lowered substantially data dimensionality, which consequently improved access methods efficiency and the CBIR system. For future work, it is recommended that a new local search into GA and the synergy between filter-based methods and the GA wrapper-based method in CBIR systems can be introduced to improve the efficiency of the proposed method. Besides those, all textual information on patients' clinical history can be incorporated into the search mechanism. Similarly, Wei et al. BIB004 proposed a set of mammogram descriptors according to the BI-RADS definition. The proposed learning approach improved the classification accuracy as high as 72% and 74%. The calcification Query-By-Example (QBE) was reported to be slightly higher than the mass QBE is more effective in extracting calcification lesions as compared to mass lesions. This study resolved that in the process of feature extraction, the calcification features are able to withstand possibilities of inaccuracies, noises and misses in spot detection. Mass lesions, in feature comparisons with those of calcification, are found to be more sensitive to segmentation imprecision and extraction. This study intends to investigate long-term learning in relevance feedback for a real-time system to develop users' search information. Ramamurthy & Chandran BIB012 , presented the texture-based image retrieval system for medical images. Gray Level Co-occurrence Matrix (GLCM) and k-means clustering algorithm were utilised in the proposed system to improve its retrieval competence. However, as the system solely used texture feature, retrieval performance was only at 50%. To characterize the texture of breast density, MammoSys was introduced based on Two-dimensional Principal Component Analysis (2DPCA). Support Vector Machine (SVM) was used to aid the retrieval process. 2DPCA was able to extract the features of the texture of breast tissues by capturing the difference between the gray level intensities among different breast densities. This research has also produced another positive consequence, i.e. all retrieved images, sharing the same view and direction, originated from BI-RADS category of the query image. This research anticipated that there could be a combination of texture attributes with the grey-level histogram, and also with other retrieval patterns, for example, breast lesions, masses and calcifications  . To abridge, different researchers have conducted study on different parts of CBIR to improve the efficiency of the CBIR system as a whole. Some of researches focused on feature extraction and combine several features to improve images retrieval. In medical, researchers usually used texture and shape features to represent the images content because medical images such as breast cancer  BIB015 [32] and brain tumor are in grayscale except for ophthalmology, pathology and dermatology  . Outside of the medical area, colour features are used because it is the most recognised feature to represent the image content as compared to other features like textures and shapes BIB013 BIB002 . Researchers also combine different features for a better characterisation of images content. Nonetheless, the efforts resulted in high dimensionality of feature set and hence lead to the research in feature selection. It is a common belief that selecting the best features that best describe the image content will help retrieve the most relevant ranked images BIB011 . There are also researchers who work on similarity measure such as new matching strategy to enhance the performance of the CBIR as well as a novel relative comparison-based similarity learning strategy to enhance CBIR performance BIB014 [36]. Zhang BIB001 stated that only colour features cannot produce good results. Therefore, the author combined the colour features with texture features. In this approach, the similar images are firstly searched based on the colour features. Then, from these searched images, the searching process is performed again using texture features. For colour features, perceptually weighted histogram is generated using L*u*v colour space. Gabor filter is used to extract the texture features. To assess the proposed techniques, experiments were performed on the MPEG-7 natural image database. Validation was done by calculating the precision and recall value for retrieval result. Likewise, Singh & Hemachandran BIB008 proposed a CBIR system using Colour moments and Gabor texture features. To extract the colour information from the image, the image is divided into three non-overlapping segments. The first three moments of the colour distributions are extracted from each segment. HSV colour space is used for the colour feature extraction. Texture features are extracted using Gabor texture descriptor and combined with the colour feature vector. They used the Canberra distance to compute the distance between the query image and the database images. For experimental purpose, they used the Coral image gallery and the results are compared with the previous CBIR techniques. To measure the performance of the proposed techniques, precision and recall were used on the performance metrics. The authors also performed experiments using only colour or texture features but the results demonstrated that only colour or texture cannot represent the image well. Yue et al., BIB005 used the co-occurrence matrix for extracting the colour and texture features. They compared the performance of local colour histogram, global colour histogram and texture features for a CBIR system. For this comparison, they designed a CBIR approach using fused colour and texture features. Sakhar & Nasre BIB006 used the local colour histogram, global colour histogram and fuzzy colour histogram for the colour feature extraction. To extract texture, they used Tammura features and the wavelet transform. Euclidean distance was used to compute the distance between the query image and the database images. Reddy and Prasad  proposed a CBIR approach using colour correlogram for colour features and multi wavelet transform for texture feature extraction. RGB colour space was used to generate the colour correlogram. They separated each colour space and then apply the correlogram in 0 0 , 45 0 , 90 0 and 135 0 . Soman et al., BIB009 used the discrete block wise cosine transform to extract the texture features and colour moments such as mean, skewness and deviation used to compute the colour feature vector. In this proposed approach, images are firstly searched based on texture features. Then, from these searched images, searching process was performed again using colour feature vector. Coral database was selected to perform the experiments and the results were compared with previous techniques.