An enhancement method for color retinal images based on image formation model

Li, Xiong; Li, Huiqi; Xu, Liang

doi:10.1016/j.cmpb.2017.02.026

Cited by 50 publications

(24 citation statements)

References 25 publications

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“…So only the green channel of the enhanced image is used in image evaluation step. The comparative analysis is carried out with [7]. The obtained results for the HRF dataset are shown in Table 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Second, this work can be extended by developing a novel feature extraction technique by which the features of retinal images such as optic disk and retinal vessel are extracted which are considered for further classification process. Image set Linear Index of Fuzziness (LIF) HE [9] CLAHE [16] Entropy [6] CEIFM [7] Proposed…”

Section: Conclusion and Future Scopementioning

confidence: 99%

“…Spatial Entropy Based Contrast Enhancement in DCT (SECEDCT) performs the global contrast enhancement by mapping each input gray-level to an output gray-level through a weight vector measured from the spatial entropy of graylevels. A slight improvement is observed in the output image due to the weight evaluation, however [7] didn't considered the spatial relationships of gray-levels, hence almost all the output gray-levels are linearly related to the input gray-levels and not kept any relation with neighbor gray-levels.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Mutual Relationship Based Retinal Image Contrast Enhancement for Efficient Diagnosis of Diabetic Retinopathy

Reddy¹,

Ravindran²,

Kakarla³

2018

IJIES

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Retinal fundus images play an important role in the diagnosis of Diabetic Retinopathy. The detailed information of retinal images like Retinal Vessels, Exudates and microaneurysms may be in low contrast and retinal image enhancement helps in the accurate diagnosis of retinal images related diseases. This paper proposes a novel contrast enhancement method to make the low-contrast retinal images more qualitative such that the detailed information will be clearer. For this purpose, the proposed mechanism considers the spatial mutual relationships between the gray-levels of image and makes the gray-levels in the output image not only linear to the gray-levels of input image but also related to the neighboring gray-levels. The proposed approach enhances the image both in global and local fashion. Spatial mutual entropy based contrast enhancement is accomplished for global contrast enhancement and Greedy contrast enhancement is accomplished for local contrast enhancement. Extensive simulations carried out over various low-contrast retinal images of High Resolution Fundus (HRF) image database shows the outstanding performance of proposed approach. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Conclusion and Future Scopementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Mutual Relationship Based Retinal Image Contrast Enhancement for Efficient Diagnosis of Diabetic Retinopathy

Reddy¹,

Ravindran²,

Kakarla³

2018

IJIES

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show abstract

“…Therefore, these images suffer from inadequate brightness and contrast, leading to low segmentation and classification results in DR assessment. Figure 2a illustrates the uneven illumination conditions of fundus images, whereas Figure 2b shows a noisy and low contrast fundus image [8]. The automated diagnosis of DR is only possible by the identification of DR-related features on the fundus images.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the contrast enhancement methods available in the literature are based on spatial (histogram) domains [9,10]. is a low-contrast and noisy image [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Qureshi

Shaheed

2019

Algorithms

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Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.

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Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Kuo

2019

Int J Imaging Syst Tech

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In order to enhance the pathological features of medical images and aid the medical diagnosis, the image enhancement is a necessary process. This study presented the Gaussian probability model combining with bi‐histogram equalization to enhance the contrast of pathological features in medical images. There are five different bi‐histogram equalizations, namely, bi‐histogram equalization (BBHE), dualistic sub‐image histogram equalization (DSIHE), bi‐histogram equalization with a plateau limit (BHEPL), bi‐histogram equalization median plateau limit (BHEPL‐D), and bi‐histogram equalization with modified histogram bins (BHEMHB). The entropy, contrast, absolute mean brightness error (AMBE), and skewness difference are used to quantize the enhancement results. From the experimental result, it is observed that the entropy and contrast of the images can be effectively enhanced by using Gaussian probability bi‐histogram equalizations, and the Gaussian probability bi‐histogram equalization median plateau limit (GPBHEPL‐D) has the best enhanced result. The proposed GPBHEPL‐D method is effective in strengthening the pathological features in medical images, so as to increase the efficiency of doctors' diagnoses and computer‐aided detection.

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An enhancement method for color retinal images based on image formation model

Cited by 50 publications

References 25 publications

Spatial Mutual Relationship Based Retinal Image Contrast Enhancement for Efficient Diagnosis of Diabetic Retinopathy

Spatial Mutual Relationship Based Retinal Image Contrast Enhancement for Efficient Diagnosis of Diabetic Retinopathy

A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

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