We concentrated on the dental X-ray image segmentation problem.A new framework combining Otsu, FCM and semi-supervised fuzzy clustering was shown.It was tested on real datasets from Hanoi Medical University in terms of accuracy.The new framework has better performance than the relevant methods.Suggestions on means and variances of the criteria of the new framework were made. Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation

An efficient framework for image retrieval using color, texture and edge features

Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive

Graphical abstractDisplay Omitted There has been an unrestrained growth of videos on the Internet due to proliferation of multimedia devices. These videos are mostly stored in unstructured repositories which pose enormous challenges for the task of both image and video retrieval. Users aim to retrieve videos of interest having content which is relevant to their need. Traditionally, low-level visual features have been used for content based video retrieval (CBVR). Consequently, a gap existed between these low-level features and the high level semantic content. The semantic differential was partially bridged by proliferation of research on interest point detectors and descriptors, which represented mid-level features of the content. The computational time and human interaction involved in the classical approaches for CBVR are quite cumbersome. In order to increase the accuracy, efficiency and effectiveness of the retrieval process, researchers resorted to soft computing paradigms. The entire retrieval task was automated to a great extent using individual soft computing components. Due to voluminous growth in the size of multimedia databases, augmented by an exponential rise in the number of users, integration of two or more soft computing techniques was desirable for enhanced efficiency and accuracy of the retrieval process. The hybrid approaches serve to enhance the overall performance and robustness of the system with reduced human interference. This article is targeted to focus on the relevant hybrid soft computing techniques which are in practice for content-based image and video retrieval.

Hybrid soft computing approaches to content based video retrieval

This paper presents a new image feature descriptor, namely directional local motif XoR patterns (DLMXoRPs) for image retrieval application. The proposed motif representation is entirely different from existing motif. The DLMXoRP presents a novel technique for the calculation of motif using 1x3 grids. The proposed motif (1x3) representation is having a flexible structure; hence it can able to extract all directional information. This flexibility is not present in the existing (2x2) motif. Further, the XoR operation is performed on the transformed new motif images which are not present in the literature (local binary patterns (LBP) and motif co-occurrence matrix (MCM)). To elevate the benefits of DLMXoRP, we compare it with the Motif XoR pattern (MXoR) which is calculated by applying the XoR operation on existing transformed motif image. The performance of the proposed method is tested by conducting three experiments on Corel-5000, Corel-10000 and MIT-VisTex databases. The results after investigation show a significant improvement in terms of average retrieval precision (ARP) and average retrieval rate (ARR) as compared to the state-of-the-art techniques for image retrieval.

Expert image retrieval system using directional local motif XoR patterns

The level histogram is used with the K-means algorithm for clustering data.The fast K-means algorithm was effectively applied to image database sets.The fast K-means algorithm improved the efficiency of the traditional K-means algorithm.The fast K-means algorithm processed images efficiently as images increases.The selection of the initial cluster centers affected the performance. In image retrieval, the image feature is the main factor determining accuracy; the color feature is the most important feature and is most commonly used with a K-means algorithm. To create a fast K-means algorithm for this study, first a level histogram of statistics for the image database is made. The level histogram is used with the K-means algorithm for clustering data. A fast K-means algorithm not only shortens the length of time spent on training the image database cluster centers, but it also overcomes the cluster center re-training problem since large numbers of images are continuously added into the database. For the experiment, we use gray and color image database sets for performance comparisons and analyzes, respectively. The results show that the fast K-means algorithm is more effective, faster, and more convenient than the traditional K-means algorithm. Moreover, it overcomes the problem of spending excessive amounts of time on re-training caused by the continuous addition of images to the image database. Selection of initial cluster centers also affects the performance of cluster center training.

Hsin-Lun Lee

Papers

Fast K-means algorithm based on a level histogram for image retrieval