TY - JOUR
KW - Contrast
KW - Medical Images
KW - Mammogram
AU - Monserrate Intriago-Pazmiño
AU - Julio Ibarra-Fiallo
AU - Adán Guzmán-Castillo
AU - Raúl Alonso-Calvo
AU - José Crespo
AB - Enhancing the visibility of medical images is part of the initial or preprocessing phase within a computer vision system. This image preparation is essential for subsequent system tasks such as segmentation or classification. Therefore, quantitative validation of medical image preprocessing is crucial. In this work, four metrics are studied: Contrast Improvement Index (CII), Enhancement Measurement Estimation (EME), Entropy EME (EMEE), and Entropy. The objective is to find the best parameters for each metric. The study is performed on five medical image datasets, three retinal fundus sets (DRIVE, ROPFI, HRF-POORQ), and two mammography image sets (MIAS, DDSM). Metrics are calculated using a binary mask image to discard the background.
Using the fundus and mask datasets, the best results were obtained with the EMEE and EMEE metrics, which achieved mean improvements of up to 186% and 75%, respectively. For mammography datasets and using masks of the region of interest, the two metrics with the highest percentage improvement were CII and EMEE, which obtained means of up to 396% and 129%, respectively. Based on the experimental results provided, we can conclude that EMEE, EME, and CII metrics can achieve better enhancement assessment in this type of medical imaging.
IS - Regular Issue
M1 - 4
N2 - Enhancing the visibility of medical images is part of the initial or preprocessing phase within a computer vision system. This image preparation is essential for subsequent system tasks such as segmentation or classification. Therefore, quantitative validation of medical image preprocessing is crucial. In this work, four metrics are studied: Contrast Improvement Index (CII), Enhancement Measurement Estimation (EME), Entropy EME (EMEE), and Entropy. The objective is to find the best parameters for each metric. The study is performed on five medical image datasets, three retinal fundus sets (DRIVE, ROPFI, HRF-POORQ), and two mammography image sets (MIAS, DDSM). Metrics are calculated using a binary mask image to discard the background.
Using the fundus and mask datasets, the best results were obtained with the EMEE and EMEE metrics, which achieved mean improvements of up to 186% and 75%, respectively. For mammography datasets and using masks of the region of interest, the two metrics with the highest percentage improvement were CII and EMEE, which obtained means of up to 396% and 129%, respectively. Based on the experimental results provided, we can conclude that EMEE, EME, and CII metrics can achieve better enhancement assessment in this type of medical imaging.
PY - 2023
SE - 117
SP - 117
EP - 126
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Quantitative Measures for Medical Fundus and Mammography Images Enhancement
UR - https://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_11.pdf
VL - 8
SN - 1989-1660
ER -