Image classification for different imaging modalities in image-guided medical diagnosis model

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Abstract

Classifying images into meaningful categories according to their imaging modalities is beginning to play an increasing important role in producing an effective medical database management system. Since medical images often represent some form of diagnosis capabilities or patient's condition, the ability to follow-up and classify these images to support doctor's diagnosis for future queries, may serve as a secondary diagnosis tool of the future. While color-based, feature-based, shape-based and content-based classification has each present its importance in classifying medical images; a universal classification technique has yet emerged for classifying different modalities together. Thus, this paper is dedicated to introduce a universal classification method which could support classifying different imaging modalities, while exploiting the current available technique concentrating on each of the important visual attributes of these methods in connection with their imaging modalities.

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