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Subscription Review Article

Review on CBIR Image Based on Color, Texture & Shape Features of Biomedical Image Applications

by 
   Kommu Naveen,    RMS. Parvathi,
Volume :  11 | Issue :  01 | Received :  November 7, 2023 | Accepted :  December 21, 2023 | Published :  April 3, 2024
DOI :  10.37591

[This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]

Keywords

Image enhancement, Bio medical, nuclear medicine, image pixel, Gamma-ray imaging

Abstract

This study seeks to understand how different image enhancing methods affect the sensitivity of contrast- based textural measures and morphological traits derived from high-resolution satellite data (three- band SPOT-5). The built-up/non-built-up detection framework is the backbone of every biomedical application. Using supervised learning while working with a low-resolution reference layer reduces uncertainty and boosts the reference layer’s quality in a roundabout way. The image’s histogram is recalculated based on contrast in order to determine textural and morphological features in light of the revised label assignments for each class. In this case study, we compare the effectiveness of several picture enhancing procedures, such as linear and de correlation stretching, by measuring their outputs against actual floor plans. The contrast of grayscale pictures is shown to be mostly determined by the mix of different spectral bands, as shown through experiments. Adjusting the contrast of a picture (either before or after combining and merging the bands) greatly aids in the extraction of useful characteristics from an otherwise low-contrast image, whereas doing so yields only little benefits for a well-contrasted one.

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