Content-Based Image Retrieval in Medical Domain: A Review <s> Feature Selection <s> The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in different times by expert MDs, consisting of 917 and 500 images of pap smear cells, respectively. Each cell is described by 20 numerical features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem. For finding the best possible performing feature subset selection problem, an effective genetic algorithm scheme is proposed. This algorithmic scheme is combined with a number of nearest neighbor based classifiers. Results show that classification accuracy generally outperforms other previously applied intelligent approaches. <s> BIB001 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Feature Selection <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> BIB002 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Feature Selection <s> Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval. <s> BIB003 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Feature Selection <s> Recently, various learning to rank approaches have been proposed in the information retrieval realm, with their promising performance in general document and web page retrieval applications. Based on these achievements, in this paper, we investigate and discuss whether learning to rank approaches can be adapted to content-based image retrieval (CBIR). Given the complex structure of image representation, it is also challenging how to design visual features for learning to rank algorithms that not only scale up well, but also model various visual modalities and the spatial distributions of local features. We answer this question by introducing some scalable visual-based ranking features for learning to rank. Specifically, we firstly adopt several well performed ad hoc ranking models to generate the bag-of-visual-words-based ranking features. Besides, images are divided into different salient regions and spatial blocks, respectively, and ranking features are extracted from each region and block. Finally, image global features-based similarities are also concatenated with the existing ranking features. Extensive experiments with three state-of-the-art learning to rank algorithms are performed over four popular image retrieval databases, together with some insightful conclusions to facilitate the adaptation of learning to rank approaches to CBIR. <s> BIB004
For each image in CBIR systems, a feature vector is processed and stored in a visual feature database. When a query is made by a user, the feature vector for this query is first computed. By using a similarity criterion, all the vectors in the visual feature database are compared to this query vector. The images that are most similar to the query image are returned to the user. It is obvious that the CBIR systems strongly depend on the characteristics extracted from the images. Features extracted from images are represented in the form of feature vector. Then, using these features, CBIR system will index and find the most similar and relevant images. Intuitively, a single feature is not able to describe the image visual content very well. Therefore, researchers have proposed a combination of several features so that the content of the images can be well-characterised. However, a substantial amount of features could affect the efficiency of the retrieval process. Large number of features normally would result in feature redundancy and irrelevant features. This would then lead to high dimensionality and eventually affect the efficiency of the retrieval process. There would be a considerable loss of the discriminative power of each feature when the number of features is added. This calls for the need for an elective feature selection algorithms to reduce the feature dimensionality. One of the key challenges in optimising the CBIR systems is feature selection. Feature selection can be defined as the process of selecting the combination of features among a given larger set that describes a particular data collection best. Feature selection has been applied as pre-processing technique in data mining, pattern recognition, classification of medical images, genomic data analysis, speech recognition and others. The function of feature selection is to decrease the features in order to generate precise results. It is therefore critical to choose the optimal features  BIB002 . Three categories of feature selection methods are filter-, wrapper-and hybrid-based. Filter-based methods utilise general characteristics of the data independently from the classifier for the evaluation process. The evaluation process is classifier-dependent in wrapper methods. Finally, hybrid models use both filtering and wrapping methods to improve the performance of the selection process. Filter methods also do not include any induction algorithms. Thus, they are not computationally expensive. There is possibility that subsets of features that are not compatible with the algorithm in the users' application are selected BIB003 . In learning to rank approach, feature selection is used, whereby only parts of raw features are chosen. Importance and similarity are two properties of each feature which are defined. In the computation of feature importance, each dimension of the feature is used to rank the database images. The ranking performance such as mean average precision (mAP) or normalised discounted cumulative gain (NDCG) is taken as its importance. To calculate feature similarity between two features, the first step is to rank the database images individually. Subsequently, the correlation of these two-ranking list would be taken as the feature similarity. Based on these two properties, the loss function of feature selection is defined as choosing the most important (high importance) and representative (low similarity) features BIB004 . De Oliveira et al.,  implemented a two-dimensional principal component analysis (PCA) to overcome the existing PCA as it is claimed to be simpler and more straightforward to use for the characterisation of breast density texture. The retrieval process was carried out using support vector machine (SVM) which solved varied learning, classification, and prediction problems. On the other hand, Marinakis et al. BIB001 used a set of evaluation functions for CBIR, which is also known as the Fitness coach (Fc). The researchers, focusing on improving the quality of each query answer by retrieving a substantial amount of pertinent images during ranking, inserted feature selection function in the GA-based algorithm.