Gaps in CBMIR using Different Methods

Content-Based Medical Image Retrieval (CBMIR) is the application of CBIR technology in medical field.
When CBMIR technology describes the image’s content, it is always extract image’s characteristics such as color, texture,
shape and spatial relation to form image’s low-level feature vector as the basis of making index and matching.
Since there are certain gaps between the description of these low-level features to medical image and the description of
doctor’s, it is always cannot get satisfied results directly use these low-level features as retrieval basis. Therefore, it is
necessary to find some kind of mapping relation between image’s low-level features and high-level semantic
information are called Semantic gaps.

Although the semantic gap & another gap is the sensory gap that describes the loss between the actual structure and
the representation in a (digital) image; might seem more tangible to bridge in the medical domain, there are many other
gaps to fill and limitations to overcome:

A. Color Gaps

In specialized fields, namely in the medical domain, absolute color or grey level features are often of very limited
expressive power unless exact reference points exist as it is the case for computed tomography images.

B. Texture Gaps

Partly due to the imprecise understanding and definition of what exactly visual texture actually is, texture measures have
an even larger variety than color measures. Some of the most common measures for capturing the texture of images are
wavelets and Gabor filters where the Gabor filters do seem to perform better and correspond well to the properties
of the human visual cortex for edge detection.

C. Local and Global Features Gaps

Both, color and texture features can be used on a global image level or on a local level on parts of the image. The
easiest way to use regional features is to use blocks of fixed size and location, so-called partitioning of the image for local
feature extraction.

D. Segmentation and Shape Features Gaps

Fully automated segmentation of images into objects itself is an unsolved problem. Even in fairly specialized domains,
fully automated segmentation causes many problems and is often not easy to realize. In image retrieval, several systems
attempt to perform an automatic segmentation of the images in the collection for feature extraction. To have an effective
segmentation of images using varied image databases the segmentation process has to be done based on the color and
texture properties of the image regions.

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