Cerebral vessel enhancement using bilateral and Hessian-based filter

Ajam, Azimah; Aziz, Azrina Abd; Asirvadam, Vijanth S.; Izhar, Lila Iznita; Muda, Sobri

doi:10.1109/icias.2016.7824109

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Furthermore, traditional medical image enhancement algorithms can be divided into spatialdomain-based enhancement methods and frequency-domain-based enhancement methods according to different scopes of image processing [4]. Medical image enhancement methods based on the spatial domain mainly include histogram algorithms [5][6][7][8][9], filter algorithms [10][11][12][13][14], and algorithms based on retinex theory [15][16][17][18][19]. The medical image enhancement algorithm based on the frequency domain converts the image from the spatial domain to the frequency domain and enhances the image with a frequency domain filter.…”

Section: Introductionmentioning

confidence: 99%

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Ren,

Li,

2023

Mathematics

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Cervical cancer is a prevalent chronic malignant tumor in gynecology, necessitating high-quality images of cervical precancerous lesions to enhance detection rates. Addressing the challenges of low contrast, uneven illumination, and indistinct lesion details in such images, this paper proposes an enhancement algorithm based on retinex and histogram equalization. First, the algorithm solves the color deviation problem by modifying the quantization formula of retinex theory. Then, the contrast-limited adaptive histogram equalization algorithm is selectively conducted on blue and green channels to avoid the problem of image visual quality reduction caused by drastic darkening of local dark areas. Next, a multi-scale detail enhancement algorithm is used to further sharpen the details. Finally, the problem of noise amplification and image distortion in the process of enhancement is alleviated by dynamic weighted fusion. The experimental results confirm the effectiveness of the proposed algorithm in optimizing brightness, enhancing contrast, sharpening details, and suppressing noise in cervical precancerous lesion images. The proposed algorithm has shown superior performance compared to other traditional methods based on objective indicators such as peak signal-to-noise ratio, detail-variance–background-variance, gray square mean deviation, contrast improvement index, and enhancement quality index.

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Section: Introductionmentioning

confidence: 99%

Cervical Precancerous Lesion Image Enhancement Based on Retinex and Histogram Equalization

Ren,

Li,

2023

Mathematics

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“…Retinal vascular enhancement in medical images [5] is achieved by using image processing methods, such as the adaptive histogram equalization method [6], unsharpened mask algorithm [7], morphological method, Hessian matrix method [8], multi-scale ltering method [9][10][11]. The global histogram equalization [12] and adaptive histogram equalization algorithms [13] are examples of image enhancement algorithms based on the histogram.…”

Section: Introductionmentioning

confidence: 99%

Research on Tissue Structure Enhancement Method of Medical Endoscope Images

Zhang

Lin

Cao

et al. 2022

Preprint

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Background:In clinical surgery, the quality of endoscopic images is degraded by noise. Noise is mainly caused by blood, illumination changes, specular reflection, smoke, etc., which will significantly reduce the quality of the image in the occluded area. Affect the doctor's judgment, prolong the operation time and increase the operation risk.Methods:This paper proposes an improved weighted guided filtering algorithm to enhance endoscopic image tissue. An unsharp mask algorithm and an improved weighted guided filter are used to enhance vessel details and contours in endoscopic images. An overall scheme of endoscopic image processing is proposed, which includes detail enhancement, contrast enhancement, brightness enhancement and highlight area removal.Results:Compared with several other algorithms, it is better than other algorithms in maintaining edges and reducing halo, and its effectiveness is proved by experiments. The peak signal-to-noise ratio and structural similarity of endoscopic images obtained by the improved algorithm in this paper are the largest. The foreground-background value Detail Variance-Background Variance has been improved.Conclusion:Our algorithm has a stronger ability to suppress noise, and is more able to maintain the structure of the original endoscopic image, which improves the details of tissue blood vessels. This paper can provide guidelines for the development of endoscopy devices.

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