Content-Based Image Retrieval in Medical Domain: A Review <s> Introduction <s> In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. <s> BIB001 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Introduction <s> The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. <s> BIB002 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Introduction <s> This paper proposes a new approach to improve similarity queries with diversity, the Diversity and Visually-Interactive method (DiVI), which employs Visual Data Mining techniques in Content-Based Image Retrieval (CBIR) systems. DiVI empowers the user to understand how the measures of similarity and diversity affect their queries, as well as increases the relevance of CBIR results according to the user judgment. An overview of the image distribution in the database is shown to the user through multidimensional projection. The user interacts with the visual representation changing the projected space or the query parameters, according to his/her needs and previous knowledge. DiVI takes advantage of the users' activity to transparently reduce the semantic gap faced by CBIR systems. Empirical evaluation show that DiVI increases the precision for querying by content and also increases the applicability and acceptance of similarity with diversity in CBIR systems. <s> BIB003 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Introduction <s> The consumer behavior has been observed to be largely influenced by image data with increasing familiarity of smart phones and World Wide Web. Traditional technique of browsing through product varieties in the Internet with text keywords has been gradually replaced by the easy accessible image data. The importance of image data has portrayed a steady growth in application orientation for business domain with the advent of different image capturing devices and social media. The paper has described a methodology of feature extraction by image binarization technique for enhancing identification and retrieval of information using content based image recognition. The proposed algorithm was tested on two public datasets, namely, Wang dataset and Oliva and Torralba (OT-Scene) dataset with 3688 images on the whole. It has outclassed the state-of-the-art techniques in performance measure and has shown statistical significance. <s> BIB004 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Introduction <s> The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval CBIR technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval CBDIR system with Relevance Feedback RF for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant melanoma, benign class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF. <s> BIB005
In recent times, the usage of digital images is becoming more popular across different sectors including medical, scientific experiments, educational and so forth. Hospitals and medical institutions are generating a great number of digital images such as x-ray, mammogram and magnetic resonance imaging (MRI) as part of their daily routine. Interpreting the medical images is certainly a complex task which requires extensive knowledge. In order to assist radiologists in interpreting the medical images, researchers have developed support systems such as Computer Aided Diagnosis (CAD) system and Content Based Image Retrieval (CBIR) system. CAD would help radiologists in diagnosis and also serve as a second opinion BIB005 . On the other hand, Content-based Image Retrieval (CBIR) uses visual content to help users browse, search and retrieve similar medical images from a database based on the user's interest [2]  . CBIR systems such as QBIC, PhotoBook or VisualSeek which help retrieve similar images would assist radiologists in analysing previous similar cases BIB001 . Researches in CBIR were initiated back in early 1990s to support the emergence of large image collection. The research area becomes more active with the advancement of the availability of multimedia technologies in the medical industry as well as the advancement in image processing and medical informatics  . Researchers study the retrieval of images in two parts: text-and visual-based. To retrieve image that is text-based, keywords are used to annotate the images. This manual annotation process is done by human and it has the potential of offering very precise information when images are well-named or annotated BIB005 . However, this process becomes much more difficult to execute as the size of image collection increases. Thus, it becomes labor-intensive and time consuming. In addition, some images cannot be annotated because they contained too much information. The other difficulty is because of the subjectivity of human perception during annotation BIB004 . In overcoming the problems of the retrieval of text-based image, researchers have shifted the focus to visual-based image retrieval. Two main components in visual-based are feature extraction and feature reduction with application of certain similarity measurement. In CBIR systems, image content includes the colour, texture, and shape. They are extracted for subsequent uses during indexing and retrieval process. These features are considered low level and are usually presented in numerical forms and assigned in feature vectors. Nonetheless, the majority of users are always more interested in specific regions rather than the entire image. Thus, most of the current CBIR systems are region-based where the features are extracted only from the Regions of Interest (ROI). Representation of the images at regional level is identified to be closer to human perception system BIB002  . In the past, the extraction of these low-level features were done by different algorithms. It is then followed by features reduction process to form a good feature representation from larger collection of image features. However, these algorithms are not able to adequately represent the semantic information or high-level features (concept) including keywords and text descriptions that are commonly used by human during interpretation. The algorithms are somehow limited when dealing with larger numbers of rich content images in the database. Researchers, in overcoming these issues, have provided resolutions to reduce the semantic gap in retrieving the images via (1) ontology highlevel concepts are defined by ontology, (2) machine learning tools associating low-level features with query concepts, with machine-learning tools and (3) relevance feedback (RF) to promote continuous learning of user interest. Besides semantic annotation, another key challenge in CBIR research is the similarity measurement. By calculating the distance between their corresponding feature descriptors (feature vectors) in the featured space, the similarity between any two images can be determined, However, there are different ways to measure image features similarity, For example, using the distance metrics (i.e. Euclidean distance) where the features are represented as a vector, or via graph matching where the objects in the images are arranged in the form of relationships among them. In addition, statistical classifiers can also be trained to categories the query image into respective known classes. This approach would overcome the semantic gap by training the similarity of measurement on known and labeled data  BIB003 . Section 2 of this paper presents a summary of research in CBIR which includes the process of image segmentation, low-level image extraction and feature selection. This section would also detail out the similarity measurement as well as the performance measurement. Section 3 reviews the related works of CBIR in medical domain and Section 4 provides the discussion on reducing the semantic gap. Finally, Section 5 ends the paper by providing future directions in CBIR research.