Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images

Mining of high dimension data for mammogram image classification is highly challenging. Feature reduction using subset selection plays enormous significance in the field of image mining to reduce the complexity of image mining process. This paper aims at investigating an improved image mining technique to enhance the automatic and semi-automatic semantic image annotation of mammography images using multivariate filters, which is the Correlation-based Feature Selection (CFS). This feature selection method is then applied onto two association rules mining methods, the Apriori and a modified genetic association rule mining technique, the GARM, to classify mammography images into their pathological labels. The findings show that the classification accuracy is improved with the use of CFS in both Apriori and GARM mining techniques

Conservation of effort in feature selection for image annotation

This paper describes an evaluation of a number of subsets of features for the purpose of image annotation using a non-parametric density estimation algorithm (described in). By applying some general recommendations from the literature and through evaluating a range of low-level visual feature configurations and subsets, we achieve an improvement in performance, measured by the mean average precision, from 0.2861 to 0.3800. We demonstrate the significant impact that the choice of visual or low-level features can have on an automatic image annotation system. There is often a large set of possible features that may be used and a corresponding large number of variables that can be configured or tuned for each feature in addition to other options for the annotation approach. Judicious and effective selection of features for image annotation is required to achieve the best performance with the least user design effort. We discuss the performance of the chosen feature subsets in comparison with previous results and propose some general recommendations observed from the work so far

Automatic annotation of X-ray images: a study on attribute selection

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification. of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space

The VIA Annotation Software for Images, Audio and Video

In this paper, we introduce a simple and standalone manual annotation tool for images, audio and video: the VGG Image Annotator (VIA). This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.Comment: to appear in Proceedings of the 27th ACM International Conference on Multimedia (MM '19), October 21-25, 2019, Nice, France. ACM, New York, NY, USA, 4 page

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Image Retrieval : Modelling Keywords via Low-level Features

Advisors: Nicolas Tsapatsoulis. Date and location of PhD thesis defense: 29 April 2014, Cyprus University of TechnologyWith the advent of cheap digital recording and storage devices and the rapidly increasing popularity of online social networks that make extended use of visual information, like Facebook and Instagram, image retrieval regained great attention among the researchers in the areas of image indexing and retrieval. Image retrieval methods are mainly falling into content-based and text-based frameworks.Although content-based image retrieval has attracted large amount of research interest, the difficulties inquerying by an example propel ultimate users towards text queries. Searching by text queries yields more effective and accurate results that meet the needs of the users while at the same time preserves their familiarity with the way traditional search engines operate. However, text-based image retrieval requires images to be annotated i.e. they are related to text information. Much effort has been invested on automatic image annotation methods [1], since the manual assignment of keywords (which is necessary for text-based image retrieval) is a time consuming and labour intensive procedure [2].In automatic image annotation, a manually annotated set of data is used to train a system for the identification of joint or conditional probability of an annotation occurring together with a certain distribution of feature vectors corresponding to image content [3]. Different models and machine learning techniques were developed to learn the correlation between image features and textual words based on examples of annotated images. Learned models of this correlation are then applied to predict keywords for unseen images [4]. In the literature of automatic semantic image annotation, proposed approaches tend to classify images using only abstract terms or using holistic image features for both abstract terms and object classes. The extraction and selection of low-level features, either holistic or from particular image areas is of primary importance for automatic image annotation. This is true either for the content-based or for the text-based retrieval paradigm. In the former case the use of appropriate low-level features leads to accurate and effective object class models used in object detection while in the latter case, the better the low- level features are, the easier the learning of keyword models is.The intent of the image classification is to categorize the content of the input image to one of several keyword classes. A proper image annotation may contain more than one keyword that is relevant to the image content, so a reclassification process is required in this case, as well as whenever a new keyword class is added to the classification scheme. The creation of separate visual models for all keyword classes adds a significant value in automatic image annotation since several keywords can be assigned to the input image. As the number of keyword classes increases the number of keywords assigned to the images also increases too and there is no need for reclassification. However, the keyword modeling incurred various issues such as the large amount of manual effort required in developing the training data, the differences in interpretation of image contents, and the inconsistency of the keyword assignments among different annotators.This thesis focuses on image retrieval using keywords under the perspective of machine learning. It covers different aspects of the current research in this area including low-level feature extraction, creation of training sets and development of machine learning methodologies. It also proposes the idea of addressing automatic image annotation by creating visual models, one for each available keyword, and presents several examples of the proposed idea by comparing different features and machine learning algorithms in creating visual models for keywords referring to the athletics domain.The idea of automatic image annotation through independent keyword visual models is divided into two main parts: the training and automatic image annotation. In the first part, visual models for all available keywords are created, using the one-against-all training paradigm, while in the second part, annotations are produced for a given image based on the output of these models, once they are fed with a feature vector extracted from the input image. An accurate manually annotated dataset containing pairs of images and annotations is prerequisite for a successful automatic image annotation. Since the manual annotations are likely to contain human judgment errors and subjectivity in interpreting the image, the current thesis investigates the factors that influence the creation of manually annotated image datasets [5]. It also proposes the idea of modeling the knowledge of several people by creating visual models using such training data, aiming to significantly improve the ultimate efficiency of image retrieval systems [6].Moreover, it proposes a new algorithm for the extraction of low level features. The Spatial Histogramof Keypoints (SHiK) [7], keeps the spatial information of localized keypoints, on an effort to overcome the limitations caused by the non-fixed and huge dimensionality of the SIFT feature vector when used in machine learning frameworks. SHiK partitions the image into a fixed number of ordered sub-regions based on the Hilbert space-Filling curve and counts the localized keypoints found inside each sub-region. The resulting spatial histogram is a compact and discriminative low-level feature vector that shows significantly improved performance on classification tasks

Medical image retrieval and automatic annotation: VPA-SABANCI at ImageCLEF 2009

Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Competition, the proposed solutions are still far from being su±ciently accurate for real-life implementations. In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation task. We use a direct and two hierarchical classification schemes that employ support vector machines and local binary patterns, which are recently developed low-cost texture descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed hierarchi-cal schemes divide the classification task into sub-problems. The first hierarchical scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that hier-archical annotation of images by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme