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Medical Image Enhancement With Brightness and Detail Preserving Using Multiscale Top-hat Transform by Reconstruction

Julio César Mello-Román, R.F. Escobar-Jiménez, Fabiola Martínez, José Luis Vázquez Noguera, Horacio Legal-Ayala, Diego P. Pinto-Roa

2020Electronic Notes in Theoretical Computer Science28 citationsDOIOpen Access PDF

Abstract

Medical imaging help medical doctors provide faster and more efficient diagnoses to their patients. Medical image quality directly influences diagnosis. However, when medical images are acquired, they often present degradations such as poor detail or low contrast. This work presents an algorithm that improves contrast and detail, preserving the natural brightness of medical images. The proposed method is based on multiscale top-hat transform by reconstruction. It extracts multiple features from the image that are then used to enhance the medical image. To quantify the performance of the proposed method, 100 medical images from a public database were used. Experiments show that the proposal improves contrast, introducing less distortion and preserving the average brightness of medical images.

Topics & Concepts

BrightnessMedical diagnosisArtificial intelligenceDistortion (music)Contrast (vision)Computer visionComputer scienceMedical imagingImage (mathematics)Contrast enhancementIterative reconstructionImage enhancementMedicineRadiologyOpticsTelecommunicationsMagnetic resonance imagingAmplifierBandwidth (computing)PhysicsImage Enhancement TechniquesAdvanced Image Fusion TechniquesImage and Signal Denoising Methods