Mouhamed Gaith Ayadi

A Model for Semantic Medical Image Retrieval Applied in a Medical Social Network

We present in this article a multimodal research model for the retrieval of medical images based on the extracted multimedia information from a radiological collaborative social network. However, opinions shared on a medical image in a medical social network constitute a textual description that requires in most of the time cleaning using a medical thesaurus. In addition, we describe the textual description and medical image in a TF-IDF weight vector using an approach of « bag-of-words ». We use latent semantic analysis to establish relationships between textual and visual terms from the shared opinions on the medical image. Multimodal modeling will search for medical information through multimodal queries. Our model is evaluated on the basis ImageCLEFmed’2015 for which we have the ground-truth. We have carried many experiments with different descriptors and many combinations of modalities. Analysis of the results shows that the model is based on two methods can increase the performance of a research system based on only one modality, either visual or textual.

A medical image retrieval scheme with relevance feedback through a medical social network

Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. Moreover, it is necessary to employ new techniques in order to effectively handle and benefit from them. This huge volume of images needs to formulate new types of queries that pose complex questions to medical social network databases. Content-based image retrieval (CBIR) stills an active and efficient research topic to manipulate medical images. In order to palliate this situation, we propose in this paper the integration of a content-based medical image retrieval method through a medical social network, based on an efficient fusion of low-level visual image features (color, shape and texture features), which offers an efficient and flexible precision. A clear application of our CBIR system consists of providing stored images that are visually similar to a new (undiagnosed) one, allowing specialist and patients to check past examination diagnoses from comments and other physicians’ annotations, and to establish, therefore, a new diagnostic or to prepare a new report of an image’s examination. To scale up the performance of the integrated CBIR system, we implement a relevance feedback method. It is an effective method to bridge the semantic gap between low-level visual features and high-level semantic meanings. Experiments show that the proposed medical image retrieval scheme achieves better performance and accuracy in retrieving images. However, we need also to verify whether our approach is considered by the specialists as a potential aid in a real environment. To do so, we evaluate our methodology’s impact in the user’s decision, inquiring the specialists about the degree of confidence in the retrieval system. By analyzing the obtained results, we can argue that the proposed methodology presented a high acceptance regarding the specialists’ interests in the clinical practice domain and can improve the decision-making process during analysis.

A framework for medical and health care databases and data warehouses conceptual modeling support

To improve their knowledge of diseases, physicians need to study and learn from their patients and from their related medical records. Physicians continue to initiate this learning process by taking into account the history of the patient’s medical problems and physical examination findings in the patient’s medical record, which illustrates the importance of medical and health care databases. In other side, the evolution of medical data volume needs to be modeled using data warehouses. A medical data warehouse is a particular database targeted toward decision support. It takes data from various medical databases and other data sources and transforms it into new structures that fit better for the task of performing the decision making. For this reason, the medical database and data warehouse models needed to produce a formal description, a conceptual schema of all the data generated in medical and health care institutions, and how all of the data were related. However, it is still difficult to find references models, since classical conceptual modeling does not incorporate the specificity of the medical field. The design phase is the most important activity in the successful building of a database and a data warehouse. To address these shortcomings, this paper proposes a new modeling framework based on an UML profile, called medical profile. This profile was proposed to standardize the task of medical data modeling, using the Unified Modeling Language (UML) extensibility mechanisms. Our proposal is aligned with model driven architecture (MDA), thus permitting to define and clarify all concepts and elements related to medical field including the medical image annotation process. To show the benefits of our profile, we develop an example related to the medical image annotation process. Finally, we also need to verify whether our framework is considered by the specialists as a potential aid. We evaluate our framework impact by inquiring the specialists about the degree of confidence in our framework. By analyzing the obtained results, we can argue that the proposed framework presented a high acceptance regarding the specialists’ interests.

A New CBIR Approach for the Annotation of Medical Images

In the medical field, images, and especially digital images, are produced in ever increasing quantities and used for diagnostics and therapy. Imaging has occupied a huge role in the management of patients, whether hospitalized or not. This gave birth of the annotation of medical image process. The annotation is intended to image analysis and solve the problem of semantic gap. Physicians and radiologists feel better while using annotation techniques for faster making decision and giving solutions to patients in a faster and more accurate way. However, medical images annotation still a hard task specially the process based Content-based image retrieval (CBIR). Recently, advances in Content Based Image Retrieval prompted researchers towards new approaches in information retrieval for image databases. In medical applications it already met some degree of success in constrained problems. For this reason, we focus in this paper on presenting to provide an efficient semi-automatic tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes for the new image annotation, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The goal of this work is to propose an approach able to compute similarity between a new medical image and old stored images. The annotator has to choose then one of the similar images and annotations related to the selected one are assigned to the new one. The idea is to apply an edge detector algorithm (Sobel algorithm) to the image and extract features from the filtered image by a color histogram. The edge to the image become likes Finger print to a human in our work. It is a search based edge. Edge representation of an image drastically reduces the amount of data to be processed, yet it retains important information about the shapes of objects in the scene. Edges in images constitute an important feature to represent their content and extraction features from filtered image improve searching of similar images, and keeping in the same time the properties of each image. The similarity measurement between images is developed based the Euclidean distance. The method can answer queries by example. The efficiency and performance of the presented method has been evaluated using the precision and the recall. The results of our experiments show high percentage of success, which is satisfactory.

Semantic medical image retrieval in a medical social network

Medical social networks have become an exchange of opinions between patients and health professionals. However, patients are anxious to quickly find a reliable analysis and a concise explanation of their medical images and express their queries through a textual description or a visual description or both sets. For this, we present in this paper a multimodal research model to research medical images based on multimedia information that is extracted from a radiological collaborative social network. Indeed, the opinions shared on a medical image in a medico-social network are a textual description which in most cases requires cleaning by using a medical thesaurus. In addition, we describe the textual description and medical image in a TF-IDF weight vector using an approach of “bag of words”. We use latent semantic analysis to establish relationships between textual terms and visual terms in shared opinions on the medical image. The multimodal modeling researches the medical information through multimodal queries. Our model is evaluated against the ImageCLEFMed’2015 baseline, which is the ground truth for our experiments. We have conducted numerous experiments with different descriptors and many combinations of modalities. The analysis of results shows that the model based on two methods can increase the performance of a research system based on a single modality, visual or textual.

A Model for Multilingual Terminology Extraction via a Medical Social Network

Abstract A growing majority of healthcare professionals, even patients, are seeking out medical social networks (MSNs) to acquire health information. The influence of MSN grows and changes daily, generating a huge volume of medical images, which are diagnosed and commented, in different languages, by several specialists. Moreover, it is necessary to employ new techniques, in order to automatically extract information and knowledge from these comments. For this reason, we propose a terms based method in order to extract the relevant concepts which can describe medical images. Significant extracted terms will be used later to facilitate their search through the medical social network site. In fact, we need to take account that existing comments are expressed in different languages to eliminate the ambiguity causing of the effectiveness’s reduction of the search function. Our methodology concentrates on the harmony between statistical methods and external multilingual semantic resources. The use of external resources will improve the efficiency of the indexing process. We evaluated our methodology by a set of experiments and a comparison study with some existing approaches in literature.

Automatic Medical Image Multilingual Indexation Through a Medical Social Network

Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. These images are diagnosed and commented, in different languages, by several specialists instantly. Moreover, it is necessary to employ new techniques, in order to automatically extract information and analyze knowledge from the huge number of comments expressing specialist’s analyzes and recommendations. For this reason, we propose a terms-based method in order to extract the relevant terms and words which can describe the medical image. Furthermore, significant extracted terms and keywords will be used later to index medical images, in order to facilitate their search through the social network site. In fact, we need to take account, in our work, that existing comments are expressed in different languages. So, it is essential to implement a multilingual indexation method to eliminate the ambiguity which will be the cause of the effectiveness’s reduction of the search function. In order to palliate this situation, we propose a multilingual mixed approach which concentrates on algorithms based on statistical methods and external multilingual semantic resources, in order to handle and to cover different languages. The use of external resources, such as semantic multilingual thesaurus, can improve the efficiency of the indexing process. Our proposed method can be applied in different languages. It is also essential to implement an auto-correction of the medical terms by using a medical dictionary. The correction of terms helps to eliminate the ambiguity which will be the cause of the reduction in the frequency of appearance of such terms. The correction of terms has taken into consideration that terms are presented in different languages. Our study is validated by a set of experiments and a comparison study with some existing approaches in literature. Experimental results have indicated that the proposed system has a superior performance compared to other systems, which is satisfactory.

A medical image retrieval scheme through a medical social network

Medical social networking sites have enabled multimedia content sharing in large volumes by allowing physicians and patients to upload their medical images. Moreover, it is necessary to employ new techniques to effectively handle and benefit from them. This huge volume of images needs to formulate new types of queries that pose complex questions to medical social network databases. Content-based image retrieval (CBIR) stills an active and efficient research topic to manipulate medical images. To palliate this situation, we propose in this paper the integration of a content-based medical image retrieval method through a medical social network, based on an efficient fusion of low-level visual image features (color, shape and texture features) which offers an efficient and flexible precision. A clear application of our CBIR system consists of providing stored images that are visually similar to a new (undiagnosed) one, allowing specialist and patients to check past exam diagnoses from comments and other physicians’ annotations, and to establish, therefore, a new diagnostic or to prepare a new report of an image’s exam. Experiments show that the proposed medical image retrieval scheme achieves better performance and accuracy in retrieving images. However, we need also to verify whether our approach is considered by the specialists as a potential aid in a real environment. To do so, we evaluate our methodology’s impact in the user’s decision, inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed methodology presented a high acceptance regarding the specialists’ interests in the clinical practice domain and can improve the decision-making process during analysis.

Automatic medical image multilingual annotation via a medical social network

Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. These images are diagnosed and commented, in different languages, by several specialists instantly. Moreover, it is necessary to employ new techniques, in order to automatically extract information and analyze knowledge from the huge number of comments expressing specialist’s analyzes and recommendations. For this reason, we propose a terms-based method in order to extract the relevant terms and words which can describe the medical image. Furthermore, significant extracted terms and keywords will be used later to index medical images, in order to facilitate their search through the social network site. In fact, we need to take account that existing comments are expressed in different languages. So, it is essential to implement a multilingual indexation method to eliminate the ambiguity which will be the cause of the effectiveness’s reduction of the search function. In order to palliate this situation, we propose a multilingual mixed approach which concentrates on algorithms based on statistical methods and external multilingual semantic resources, in order to handle and to cover different languages. The use of external resources, such as semantic multilingual thesaurus, can improve the efficiency of the indexing process. The proposed method can be applied in different languages. Our study is validated by a set of experiments and a comparison study with some existing approaches in literature. Experimental results have indicated that the proposed system has a superior performance compared to other systems. Finally, we need also to verify whether the system is considered by the specialists as a potential aid. We evaluate its impact, by inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed system presented a high acceptance and viability rate regarding the specialists’ interests in the annotation practice domain.

Content Modelling in Radiological Social Network Collaboration

We present in this paper a model representation of a report extracted from a radiological collaborative social network, which combines textual and visual descriptors. The text and the medical image, which compose a report, are each described by a vector of TF-IDF weights following an approach “bag-of-words”. The model used, allows for multimodal queries to research medical information. Our model is evaluated on the basis imageCLEFMed’ 2015 for which we have the ground truth. Many experiments were conducted with various descriptors and many combinations of modalities. Analysis of the results shows that the model, which is based on two modalities allows to increase the performance of a search system based on only one modality, that it be textual or visual.