Mohammed Elmogy

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

OBJECTIVE Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. METHODS Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. MATERIAL This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. RESULTS The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. CONCLUSION Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems.

An Ontological Case Base Engineering Methodology for Diabetes Management

Ontology engineering covers issues related to ontology development and use. In Case Based Reasoning (CBR) system, ontology plays two main roles; the first as case base and the second as domain ontology. However, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. This paper proposes an ontology engineering methodology to generate case bases in the medical domain. It mainly focuses on the research of case representation in the form of ontology to support the case semantic retrieval and enhance all knowledge intensive CBR processes. A case study on diabetes diagnosis case base will be provided to evaluate the proposed methodology.

Robust real-time landmark recognition for humanoid robot navigation

Landmark recognition is identified as one important research area in robot navigation systems. It is a key feature for building robots capable of navigating and performing tasks in human environments. However, current object recognition research largely ignores the problems that the mobile robot context introduces. We developed a landmark recognition system which is used by a humanoid robot to identify landmarks during its navigation. The humanoid landmark recognition system is based on a two-step classification stage which is robust and invariant towards scaling and translations. Also, it provides a good balance between fast processing time and high detection accuracy. An appearance-based classification method is initially used to provide the rough initial estimate of the landmark. It is followed by a refinement step using a model-based method to estimate an accurate classification of the object. The goal of our work is to develop a rapid, robust object recognition system with a high detection rate that can actually be used by a humanoid robot to recognize landmarks during its navigation.

A Decision Support System for Diabetes Mellitus Management

Diabetes mellitus is considered as a dangerous chronic disease. Diagnosis is the first step in its management. Clinical decision support system (CDSS) for diabetes diagnosis improves its detection and decreases the opportunity for its complications. However, its diagnosis is a theory-less problem. Case-based reasoning (CBR) is a problem-solving paradigm that uses past experiences to solve new problems. Integration of CBR and formal ontologies enhances the intelligence of this paradigm. Utilizing patients’ electronic health records (EHRs) for building case-base knowledge solves the problem of knowledge acquisition bottleneck; however, preparation steps are required. Moreover, using standard medical ontologies, such as SNOMED-CT, enhances the interoperability and integration of CDSS with the healthcare system. If ontology-based CBR systems utilize vague or imprecise knowledge, the semantic effectiveness is further improved. This paper proposes an advanced and complete fuzzy-ontology-based CBR framework that manages and utilizes imprecise knowledge. We implement the most critical steps in CBR (i.e., case representation and retrieval). The implemented framework has been tested on the diabetes diagnosis problem using a case-base of 60 real cases from The EHR of the Mansoura University Hospitals, Mansoura, Egypt. The proposed system has an accuracy of 97.67%.

Image Stitching System Based on ORB Feature-Based Technique and Compensation Blending

The construction of a high-resolution panoramic image from a sequence of input overlapping images of the same scene is called image stitching/mosaicing. It is considered as an important, challenging topic in computer vision, multimedia, and computer graphics. The quality of the mosaic image and the time cost are the two primary parameters for measuring the stitching performance. Therefore, the main objective of this paper is to introduce a high-quality image stitching system with least computation time. First, we compare many different features detectors. We test Harris corner detector, SIFT, SURF, FAST, GoodFeaturesToTrack, MSER, and ORB techniques to measure the detection rate of the corrected keypoints and processing time. Second, we manipulate the implementation of different common categories of image blending methods to increase the quality of the stitching process. From experimental results, we conclude that ORB algorithm is the fastest, more accurate, and with higher performance. In addition, Exposure Compensation is the highest stitching quality blending method. Finally, we have generated an image stitching system based on ORB using Exposure Compensation blending method.

Online motion planning for HOAP-2 humanoid robot navigation

Autonomous robot navigation is becoming an increasingly important research topic for mobile robots. In the last few years, significant progress has been made towards stable robotic bipedal walking. This is creating an increased research interest in developing autonomous navigation strategies which are tailored specifically to humanoid robots. Efficient approaches to perception and motion planning, which are suited to the unique characteristics of biped humanoid robots and their typical operating environments, are receiving special interest. In this paper, we present a time-efficient motion planning system for a Fujitsu HOAP-2 humanoid robot. The sampling based algorithm is used to provide the robot with minimal free configuration space which is sampled to extract the robot path. For collision detection, a cylinder model is used to approximate the trajectory for the body center of the humanoid robot during navigation. It calculates the actual distances required to execute different actions of the robot and compares them with the distances to the nearest obstacles. The A* search algorithm is then implemented to find smooth and low-cost footstep placements of the humanoid robot within the resulting configuration space. The proposed hybrid algorithm reduces searching time and produces a smoother path for the humanoid robot at a low cost.

A new NMF-autoencoder based CAD system for early diagnosis of prostate cancer

In this paper, we propose a novel non-invasive framework for the early diagnosis of prostate cancer from diffusion-weighted magnetic reasoning imaging (DW-MRI). The proposed approach consists of three main steps. In the first step, the prostate is localized and segmented based on a new level-set model. This model is guided by a stochastic speed function that is derived using nonnegative matrix factorization (NMF). The NMF attributes are calculated using information from the MRI intensity, a probabilistic shape model, and the spatial interactions between prostate voxels. In the second step, the apparent diffusion coefficient (ADC) of the segmented prostate volume is mathematically calculated for different b-values. To preserve continuity, the calculated ADC values are normalized and refined using a Generalized Gauss-Markov Random Field (GGMRF) image model. The cumulative distribution function (CDF) of refined ADC for the prostate tissues at different b-values are then constructed. These CDFs are considered as global features which can be used to distinguish between benign and malignant tumors. Finally, a deep learning auto-encoder network, trained by a non-negativity constraint algorithm (NCAE), is used to classify the prostate tumor as benign or malignant based on the CDFs extracted from the previous step. Preliminary experiments on 42 clinical DW-MRI data sets resulted in 97.6% correct classification (sensitivity = 100% and specificity = 95.24%), indicating the high accuracy of the proposed framework.

Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words

Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query, SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The experimental results reveal that this system performs well on two different standard datasets.

Case Based Reasoning: Case Representation Methodologies

Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the proper operation of the overall CBR system. In this paper, we survey and evaluate all of the existing case representation methodologies. Moreover, the case retrieval and future challenges for effective CBR are explained. Case representation methods are grouped in to knowledge-intensive approaches and traditional approaches. The first group overweight the second one. The first methods depend on ontology and enhance all CBR processes including case representation, retrieval, storage, and adaptation. By using a proposed set of qualitative metrics, the existing methods based on ontology for case representation are studied and evaluated in details. All these systems have limitations. No approach exceeds 53% of the specified metrics. The results of the survey explain the current limitations of CBR systems. It shows that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.

MRI Brain Tumor Segmentation System Based on Hybrid Clustering Techniques

In this paper, we developed a medical image segmentation system based on hybrid clustering techniques to provide an accurate detection of brain tumor with minimal execution time. Two hybrid techniques have been proposed in our proposed medical image segmentation system. The first hybrid technique is based on k-means and fuzzy c-means (KFCM) while the second is based on k-means and particle swarm optimization (KPSO). We compared the two proposed techniques with k-means; fuzzy c-means, expectation maximization, mean shift, and particle swarm optimization using three different benchmark brain data sets. The results clarify the effectiveness of our second proposed technique.