The importance of global features in the analysis of tissue images cannot be overemphasized especially in texture image classification and retrieval. This paper presents different techniques for detection, classification and analysis of diseases pattern in medical images. The research work studies the structure of tissue images; and extracts the similarity features characterized by the Holder exponent for pattern classification. Features from multi-fractal descriptors have been extracted and combined with features from fractal descriptors to generate new descriptors for efficient analysis of images. The experimental procedures have been tested with different extracted features during the classification process to determine the appropriate image features that could yield maximum detection accuracy. The results showed that the descriptors extracted from different features could improve the performance of the models. Our findings in this paper have greatly demonstrated the importance of global features in the analysis of tissue pattern.

This paper presents two descriptors to tackle the existing problems in medical imaging by providing more information to describe different textural structures of digital images. The proposed global and local descriptors can provide more accurate analysis of medical features by using hybrid concatenation approach. Several mathematical models in the form of local and global descriptors have been developed and used in the computation and analysis of medical problems. The experimental results showed that both local and global features are very useful in detection and analysis of biomedical features. The results also indicate that the global descriptor outperforms the earlier approaches and demonstrates high discriminating power and robustness of combined features for accurate classification of CT images.

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Ebola virus disease is a hemorrhagic fever that has a near 100% fatality rate if not detected on time and properly managed. Between December 2013 and September 6, 2015, Africa and few other countries in the west witnessed the worst outbreak of the disease with 28,183 confirmed cases out of which 11,306 died. In an untiring effort to eradicate this pandemic, scientists have sought different measures for treating and caring for infected persons while also preventing further transmission of the disease. Hitherto, there still exist cases of transmission among humans especially patient-to-health care provider transmission. This project addresses the problem using visual programming language for diagnosing the disease. Requirement gathering exercise and specification was done through interviews with health care providers, site visit to Ebola treatment center and review of literature and Ebola registries. Expert system concepts with Visual Basic programming language were adopted in the development of the system. Reliable inferences were made regardless of the Ebola case scenario that was used in the testing of the expert system. The system showed that reduction in person-to-person transmission of Ebola virus disease can be achieved if probable suspects are identified and diagnosed on time using computer applications that eliminates physical contact with suspects or infected materials and fluids. For confirmed suspects, the system recommends laboratory test as a final proof of the infection. Using an interactive diagnosis expert system for detecting Ebola cases is a fast and safer avenue through which Ebola transmissions; especially human-to-human transmissions could be reduced.