Development of multiscale retinex algorithm for medical image enhancement based on multi-rate sampling

Precise characterization and analysis of corneal nerve fiber tortuosity are of great importance in facilitating examination and diagnosis of many eye-related diseases. In this paper we propose a fully automated method for image-level tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classification. The image enhancement component is based on an extended Retinex model, which not only corrects imbalanced illumination and improves image contrast in an image, but also models noise explicitly to aid removal of imaging noise. Afterwards, we take advantage of exponential curvature estimation in the 3D space of positions and orientations to directly measure curvature based on the enhanced images, rather than relying on the explicit segmentation and skeletonization steps in a conventional pipeline usually with accumulated preprocessing errors. The proposed method has been applied over two corneal nerve microscopy datasets for the estimation of a tortuosity level for each image. The experimental results show that it performs better than several selected state-of-the-art methods. Furthermore, we have performed manual gradings at tortuosity level of four hundred and three corneal nerve microscopic images, and this dataset has been released for public access to facilitate other researchers in the community in carrying out further research on the same and related topics.

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“…To estimate L and R, the conventional methods [29], [30] usually build and optimize the following objective function:…”

Section: A Preliminariesmentioning

confidence: 99%

Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy

Zhao

Zhang

Pereira

et al. 2020

IEEE Trans. Med. Imaging

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“…Therefore, image enhancement is often required for the aesthetic and pragmatic purposes. Recently, Retinex theory has been developed and used to enhance images under dark lighting conditions [27]- [30]. For the EAR system player image enhancement is achieved by applying the MSR [30].…”

Section: Player Image Enhancement Using Multi Scale Retinex (Msr) Theorymentioning

confidence: 99%

EAR: Enhanced Augmented Reality System for Sports Entertainment Applications

Mahmood

Ali²,

Muhammad

et al. 2017

KSII TIIS

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Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.

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“…In recent years, the Retinex theory is widely used in the medical image processing [7]. Many algorithms based on Retinex theory such as single-scale Retinex (SSR) [8], multiscale Retinex (MSR) [9], McCann99 [10], and Frackle-McCann [11] have been developed for image enhancement. There are also many other methods to enhance high dynamic range (HDR) images [12–14].…”

Section: Introductionmentioning

confidence: 99%

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

Sun

Jiang

Zhu

et al. 2016

BioMed Research International

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Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

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Development of multiscale retinex algorithm for medical image enhancement based on multi-rate sampling

Cited by 14 publications

References 8 publications

Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy

Automated Tortuosity Analysis of Nerve Fibers in Corneal Confocal Microscopy

EAR: Enhanced Augmented Reality System for Sports Entertainment Applications

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

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