As medical images are fuzzy, edge detection based on texture characteristics is comparatively effective than intensity based techniques. A new methodology is described for texture edge detection in medical images that is applicable across modalities. We use a multi-scale filter to capture texture edge information. An experimental prototype based on the proposed methodology provides a test bed for comparison with a popular edge detection technique.
Recent advances in healthcare such as Evidence Based Medicine (EBM) and Clinical Decision Support Systems (CDSS) requires practitioners to frequently access archived historical healthcare literatures and images. As the majority of healthcare literatures contain images such as medical images, clip arts, waveforms, flow charts and block diagrams, in this paper we present the use of Content Based Image Retrieval (CBIR) for efficient healthcare literature search and retrieval. We introduce a novel shape based feature called Fourier Edge Orientation Autocorrelogram (FEOAC) for search and retrieval of healthcare literatures. Scale and translation invariant Edge Orientation Autocorrelogram (EOAC) feature is made rotation invariant by applying Fourier transform. This Fourier based shape feature also reduces the feature set dimension enabling faster retrieval of document images in large databases. Experimental results show that FEOAC outperforms EOAC for search and retrieval of healthcare document images, with improved precision and recall rates.
In this paper, we propose an approach for representing both shape and texture information in an image using a single hybrid feature descriptor for Content Based Image Retrieval. Towards this, we compute the gradient magnitude of the input image prior to deriving features. Feature extraction is then performed using the responses from a bank of Gabor filters. Here, we exploit the fact that shape corresponds to the high spatial frequency content in the image whereas natural texture information predominantly lies within low to mid-range frequencies. This approach helps in better localization of characteristic texture as well as shape, due to spread of energy towards high frequencies in spectral domain. Moment invariants are extracted from Gabor filter responses which yield better retrieval performance than conventional statistical features. Experimental results show that this approach has relatively improved retrieval performance on Corel image data set when compared with recent approaches in the literature. Further experiments were also performed on a medical image dataset with 95.4 percent precision and 74.6 percent recall.
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