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Abstract
Medical images various formats are increasing simultaneously with the increase in technology, where those
images are created, transmitted and analyzed. The different ways
in which this data is stored and formatted between the conflicting
modalities are necessary to be restricted.
Even though the literature lists several successful systems
for content-based image retrieval and image management methods, they have been unable to signify the inroads in routine medical informatics. Still there is a significant increase in the use of medical images in various but vital areas such as; clinical
medicine, disease research, and education.
This study includes the use of a description of the primitive
features of an image; texture, color, and shape. These features are
extracted and used as the basis for a similarity check between
images. The algorithms used to calculate the similarity between
extracted features.
This work performs a simple color-based search in an image database for an input query image, using color histograms. It then compares the color histograms of different images using the Quadratic Distance Equation. Further enhancing the search, the
research performs a texture-based search in the color results, using
wavelet decomposition and energy level calculation. It then compares the texture features images in database with texture features of query image using the Euclidean Distance Equation. A more detailed step would further enhance these texture results,
using a shape-based search.
This research introduces a new approach to image retrieval based on color, texture, and shape by using pyramid structure wavelet. As well as showing the method of combining these
features for content based image retrieval to generate a description
of images. The major advantage of such an approach is that little
human intervention is required. However, most of these systems
only allow a user to query using a complete image with multiple
regions and are unable to retrieve similar looking images based on
a single region. Experimental results of the query system on different test image databases are given.
This work introduces a comparative study between col or, texture, shape and pyramid structure wavelet technique and col or, texture by using segmentation and generates the Receiving Operating characteristic Curve (ROC) to assess the results. The
area under the curve when use color, texture, shape and pyramid
structure wavelet technique are 0.58, 0.68, 0.74, 0.8 respectively
where the area under the curve when use color and texture using
segmentation are 0.49, 0.58 respectively