Noise reduction on mammographic phantom images

A noise reduction scheme on digitized mammographic phantom images is presented. This algorithm is based on a direct contrast modification method using an optimal function which is obtained by means of the mean squared error as a criterion. Computer simulated images containing objects similar to those observed in the phantom are built to evaluate the performance of the algorithm. Noise reduction results obtained on both simulated and real phantom images show that the developed method may be considered as a good pre-processing step from the point of view of automating phantom film evaluation by means of image processing

Segmentation par une approche statistique de la zone avasculaire centrale sur des angiographies rétiniennes numériques

Cette thèse traite de la détection automatique de la zone avasculaire centrale (ZAC) au cours d'une rétinopathie diabétique (RD) et du suivi objectif de cette maladie, par application du traitement d'image sur des angiographies numériques. A cet effet, et après avoir testé différentes méthodes classiques de traitement d'image qui se sont avérées inadaptées pour résoudre ce problème, deux méthodes algorithmiques originales ont été conçues. La première est basée sur le test du rapport de vraisemblance maximale, elle nécessite une étape de prétraitement par décomposition en valeurs singulières ; la seconde utilise les champs aléatoires de Markov. Elles exploitent toutes les deux les propriétés statistiques des informations issues d'images réalisées sur des patients. La détection Bayésienne du contour de la ZAC obtenue est très encourageante. Nous pensons, à travers ce travail, apporter à la communauté des ophtalmologues une aide assistée par ordinateur dans la détection et le suivi de la rétinopathie diabétiqueAIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocSudocFranceF

doi:10.1155/2007/49482 Research Article Statistical Segmentation of Regions of Interest on a Mammographic Image

This paper deals with segmentation of breast anatomical regions, pectoral muscle, fatty and fibroglandular regions, using a Bayesian approach. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with characteristics (density, texture, etc.) of regions of interest on digitized mammograms. Novelty in this paper consists in applying and adapting Markov random field for detecting breast anatomical regions on digitized mammograms whereas most of previous works were focused on masses and microcalcifications. The developed method was tested on 50 digitized mammograms of the mini-MIAS database. Computer segmentation is compared to manual one made by a radiologist. A good agreement is obtained on 68 % of the mini-MIAS mammographic image database used in this study. Given obtained segmentation results, the proposed method could be considered as a satisfying first approach for segmenting regions of interest in a breast. Copyright © 2007 Mouloud Adel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1

Noise reduction on mammographic phantom images

A noise reduction scheme on digitized mammographic phantom images is presented. This algorithm is based on a direct contrast modification method using an optimal function which is obtained by means of the mean squared error as a criterion. Computer simulated images containing objects similar to those observed in the phantom are built to evaluate the performance of the algorithm. Noise reduction results obtained on both simulated and real phantom images show that the developed method may be considered as a good pre-processing step from the point of view of automating phantom film evaluation by means of image processing

Filtering noise on mammographic phantom images using local contrast modification functions

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Detection of the Foveal avascular zone on retinal angiograms using Markov random fields

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Statistical-based tracking technique for linear structures detection: Application to vessel segmentation in medical images

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