A novel color feature for the improvement of pigment spot extraction in iris images
Feature extraction plays a vital role in the segmentation of regions of interest in medical images. While histograms offer a reliable method for analyzing color properties, the challenge of defining the pigment spot color has motivated the search for a practical feature for extraction. Consequen...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
University of Portsmouth
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28820/2/0277428112024141861298.pdf http://eprints.utem.edu.my/id/eprint/28820/ https://www.joig.net/2024/JOIG-V12N4-410.pdf |
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| Summary: | Feature extraction plays a vital role in the segmentation of regions of interest in medical images. While
histograms offer a reliable method for analyzing color
properties, the challenge of defining the pigment spot color
has motivated the search for a practical feature for extraction.
Consequently, analyzing the image using histograms and the
HSV (Hue, Saturation, Value) color space led to the groundbreaking discovery of a reliable color feature and an
exciting opportunity for pigment spot extraction. This study
utilized 131 pigment spot images from the Miles Research
datasets. The Region of Interest (ROI) was determined using
a histogram color-based saturation intensity component,
revealing new findings of thresholds ranging from 0.70 to
0.90. The results indicate that the proposed method achieved
a Detection Rate (DR) of 37.1% (49 images), a False
Acceptance Rate (FAR) of 14.5% (19 images), and a False
Rejection Rate (FRR) of 48.4% (63 images). While the
detection rate shows room for improvement, the proposed
method significantly reduces the FAR to 14.5%, compared to
64.8% and 65.3% in color-based segmentation and simple
color detection, respectively. This newfound feature
contributes to improved accuracy and efficiency in medical
image analysis, facilitating better patient diagnosis and
treatment planning. |
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