Since the histological features can have any orientation in the image, the Gabor features of the eight orientations undergoes Fourier transform to reduce the rotation variance. To increase the accuracy rate of the Gabor features, a large 128x128 window was also tested. The window size employed for determining LMS regions is set to 64x64 pixels. Two methods have been investigated, respectively gray-level histogram and Gabor features. The classification rate is higher but it may be partially due to the subdivision of large intestine mucosa into 5 classes.
Further study is needed to improve the performance.
Conclusions Intelligent image retrieval and browsing requires a combination of prior medical domain knowledge, image content and image annotation analysis. This in turn calls for the development of computational mechanisms and architecture which support hypothesis generation and reasoning, iconic feature extraction from images, and semantic contents generation and analysis. When detecting or generating these features, the higher the level of processing, the more domain specific or context information is needed. An intelligent image database system should integrate all these components to complement each other. This paper presents our approach and result of using both Gabor texture and histograms features derived from an input medical image to assign histologically meaningful labels to different regions within the image.