Content-Based Image Retrieval in Medical Domain: A Review <s> Reducing The Semantic Gap <s> Accurate diagnosis is crucial for successful treatment of the brain tumor. Accordingly in this paper, we propose an intelligent content-based image retrieval (CBIR) system which retrieves similar pathology bearing magnetic resonance (MR) images of the brain from a medical database to assist the radiologist in the diagnosis of the brain tumor. A single feature vector will not perform well for finding similar images in the medical domain as images within the same disease class differ by severity, density and other such factors. To handle this problem, the proposed CBIR system uses a two-step approach to retrieve similar MR images. The first step classifies the query image as benign or malignant using the features that discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features that distinguish the subclasses. In order to provide faster image retrieval, we propose an indexing method called clustering with principal component analysis (PCA) and KD-tree which groups subclass features into clusters using modified K-means clustering and separately reduces the dimensionality of each cluster using PCA. The reduced feature set is then indexed using a KD-tree. The proposed CBIR system is also made robust against misalignment that occurs during MR image acquisition. Experiments were carried out on a database consisting of 820 MR images of the brain tumor. The experimental results demonstrate the effectiveness of the proposed system and show the viability of clinical application. <s> BIB001 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Reducing The Semantic Gap <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> Reducing The Semantic Gap <s> Content-based image retrieval (CBIR) in large medical image archives is a challenging and necessary task. Generally, different feature extraction methods are used to assign expressive and invariant features to each image such that the search for similar images comes down to feature classification and/or matching. The present work introduces a new image retrieval method for medical applications that employs a convolutional neural network (CNN) with recently introduced Radon barcodes. We combine neural codes for global classification with Radon barcodes for the final retrieval. We also examine image search based on regions of interest (ROI) matching after image retrieval. The IRMA dataset with more than 14,000 x-rays images is used to evaluate the performance of our method. Experimental results show that our approach is superior to many published works. <s> BIB003
Semantic is one of the crucial issues in CBIR system. Researchers who worked on low level features have employed several techniques to reduce the semantic gap in CBIR. Based on the previous works, some researchers improved the efficiency of CBIR systems by means of ontology, machine learning tools as well as employing Relevance Feedback (RF) to ensure high relevant images. High-level features are semantically meaningful labels which describe the shape of a mass that can be round, oval, lobular or irregular [43] . Relevance feedback is a powerful tool and online learning to retrieve most relevant images. This strategy ask user to give some feedbacks on the results returned in the previous query round and come up with a better result based on these feedbacks. A variety of relevance feedback techniques designed to bride the semantics gap between low level visual features and high level semantic concept of each image. The general process of Relevance Feedback is as follows: First user labels a number of relevant images as positive feedback and a number of irrelevant images as negative feedback from retrieved images. Then the CBIR system then refines its retrieval procedure based on these labeled samples. These processes carried out iteratively. RF techniques are classified into two categories: that is query movement and biased subspace learning. In this biased subspace learning, all positive samples are alike and each negative sample in negative in its own way  . Furthermore, these descriptors form a simple vocabulary, the so-called 'object-ontology' that provides a qualitative definition of high-level query concepts. Database images can be classified into different categories by mapping such descriptors to high-level semantics (keywords) based on our knowledge 7 . Researchers could automate the images according to this ontology. In medical, ontology is developed based on some standard such as the BI-RADS standard. Similar work has been done to annotate the mammogram images using Mammography Annotation Ontology (MAO) that was developed via MAO and was also based on BI-RADS standard  . DeOliveira et al.,  also used SVM for 9 1234567890 ''"" 1st International Conference on Green and Sustainable Computing (ICoGeS) 2017 IOP Publishing IOP Conf. Series: Journal of Physics: Conf. Series 1019 (2018) 012044 doi :10.1088/1742-6596/1019/1/012044 retrieval task as it is able to solve a variety of learning, classification and prediction problems. For medical applications, Liu et al., BIB003 presented a new image retrieval method. It utilises a convolutional neural network (CNN) that is merged with neural codes for global classification and equipped with the latest Radon barcodes for the final retrieval. The researchers investigated image search based on regions of interest (ROI) that are matched after image retrieval. Relevance Feedback (RF) is another approach to reduce semantic gap in retrieving images. RF is a supervised learning technique because user has to give feedback by labeling the returned images. In conventional textbased information retrieval systems, relevance feedback is a very reliable technique used. This technique regulates automatically existing query relevant to the preceding objects retrieved through information fed back by users to the system. This allows users to have a query which is more attuned based on their information need. The main aim of the medical image databases therefore is to organize, search, and index the vast corpus of medical images. It is therefore crucial to have, an intelligent system that enables the capture, recognition, and comprehension of the intricate content of medical images. For these tasks to be fulfilled, content-based retrieval, by which a number of techniques used in medical images has been developed, is a promising approach. In spite of the current progress, medical content-based image retrieval has the potential to develop further and there should be more research into this area. Eventually, a competent image database, together with an intelligent retrieval system will be able to improve clinical treatment, and offer a platform for better medical research and education BIB001 . To enhance performance in relevance feedback scheme, Wang et al., BIB002 proposed a Feature Line Embedding Biased Discriminant Analysis (FLE-BDA). The margin between relevant and irrelevant samples in the local neighbourhood is maximised by reducing the gap between the query and relevant images. This subspace learning method allows a linear transformation matrix from relevant or irrelevant images that is used in dimensionality reduction. The process of retrieval comprises: 1)Keying-in a query image into the IR system. Gallery images are ranked subsequent to the calculation of the similarity values. 2) The labelling of the relevant or irrelevant images as per users'preference 3) Adopting users' feedback for a new transformation. 4) Re-ranking the gallery images to acquire the retrieval results in the next round. Two labels are ascribed to the top-ranking images according to users' preference. Users' preference is signified by the feedback of relevant or irrelevant labels. The within class scatter is calculated from the image samples with positive labels, while the between-class scatter is calculated from those with negative labels. Based on these assigned labels, the within class and between-class weighted graphs are constructed for maximizing the margin of relevant and irrelevant samples. Then new distance between query and images are calculated. The advantages are dimensionality reduction, solve singular problem in the high dimensional space, increases generalization and robustness using Laplacian regularization. The disadvantage is computational complexity is very high due to the largescale dataset. Early relevance feedback method has been classified into the query point movement and the reweighting method. Basically, the query point movement method enhances the estimation of the 'ideal query point' by shifting positive examples nearer to it, and negative ones afar. Fundamentally, the reweighting method is to amend the weight of each image feature element. These methods strongly assume that the target class, with an elliptical shape, significantly limits their performance. The main goal of this system is to get the best relevant and similar image as preferred by user.