Content-Based Image Retrieval in Medical Domain: A Review <s> Image Segmentation <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> BIB001 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Image Segmentation <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> BIB002 </s> Content-Based Image Retrieval in Medical Domain: A Review <s> Image Segmentation <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> BIB003
The selection of the types of features, global (involving the colour and texture of the histograms and the entire image layout) and local (the colour, texture and shapes of sub-images, segmented regions Colour: Colour features are designed based on colour space. Colour representations that are commonly used in RBIR are RGB, LAB and LUV, HSV (HSL), YCrCb, and Hue-Min-Max-Difference (HMMD). All of these are designed to be closer to human perception. They are represented using colour-covariance matrix, colour histogram, colour correlogram, dominant colour descriptor, colour moments, colour coherent vector or colour co-occurrence matrix. Colour features are reported to be very stable and robust due to their resilience towards rotation, translation and changes during scaling  . However, most medical images are in grayscale except for colour photographs that are used for diagnosis in ophthalmology, pathology and dermatology BIB003  . Bunte et al.  used elective colour features to retrieve similar skin lesions when comparing to an actual case to verify the diagnosis or confer with similar symptoms. The colour feature representations used are limited to rank matrix learning vector quantisation (LiRaMLVQ) and a Large Margin Nearest Neighbor (LMNN) approach. They performed a comparison on the retrieval rates achieved with extracted and original features for eight different standard colour spaces, and the research has achieved a significant result for each colour space. Shape: Shape features, in comparison to colour and texture features, have not been used commonly. Past research have shown that it is challenging to apply shape features as compared to textual and colour features as they may be inaccurate during the segmentation process  . Shape features are useful in cases where images comprise objects that can evidently be separated from the background. Shape features include aspect ratio, circularity, Fourier descriptor, moment invariants and Zernike moments. In retrieving medical images, shape features are the best descriptors in detecting disease, lesion or mass. Wei et al. BIB001 applied features of shape and margin using Zernike moments to identify mass lesions in the mammogram. The result showed that the retrieval system was capable of achieving the highest precision among all mass lesion types when retrieving round-shaped and circumscribed margin masses, Texture: Texture refers to the visual patterns that contain homogeneity properties and is not a result of the presence of only a single colour or intensity. Texture features are represented by using the graylevel co-occurrence matrix, Tamura feature, wavelet coefficients, gabor filter and Haralick's feature. For Tamura feature, six visual texture properties which include coarseness, contrast, directionality, line likeness, regularity, and roughness are used. In Haralick's approach, the texture features are represented using the co-occurrence matrix based on the orientation and distance between image pixels. Meaningful statistics are then extracted from the matrix as the texture representation  . In medical domain, researchers usually use texture features because organs and tissue anomalies are well characterised by texture properties BIB003 BIB001 . Tissues are expected to have consistent and homogeneous texture. Thus, it is the best option to discriminate among organ tissues in CBIR system, for intances,texture features have been used for brain tumor diagnosis BIB002 and mass lesion detection in mammogram images  .