Detection and Inpainting of Specular Reflection in Colposcopic Images with Exemplar-based Method

Wang, Xiaoxia; Li, Ping; Du, Yongzhao; Lv, Yuchun; Chen, Yinglu

doi:10.1109/icasid.2019.8925202

Cited by 9 publications

(16 citation statements)

References 15 publications

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“…Colonoscopy [ 25 ], thoracoscopy [ 26 ], laparoscopy [ 27 ], and more researches on eliminating SR regions in these images [ 28 , 29 ] can be used for reference. In the report of Wang et al [ 30 ], they used Arnold's method [ 25 ] in colonoscopy images and combined it with the exemplar-based method to eliminate SR regions in colposcopic images, showing better effect.…”

Section: Related Workmentioning

confidence: 99%

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Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

Wang

et al. 2021

Journal of Healthcare Engineering

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Objective. To explore an inpainting method that can balance texture details and visual observability to eliminate the specular reflection (SR) regions in the colposcopic image, thus improving the accuracy of clinical diagnosis for cervical cancer. Methods. (1) To ensure smoothness, Gaussian Blur and filling methods are applied to the global image. (2) Striving to preserve the anatomical texture details of the colposcopic image as much as possible, the exemplar-based method is applied to local blocks. (3) The colposcopic images inpainted in the previous two steps are integrated, so that important information of non-SR regions is preserved based on eliminating SR regions. Results. In the subjective visual assessment of inpainting results, the average of 3.55 ranks first in the five comparison sets. As to the clinical test, comparing the diagnosis results of 6 physicians before and after eliminating SR regions, the average accuracy of two kinds of classifications increased by 1.44% and 2.03%, respectively. Conclusions. This method can effectively eliminate the SR regions in the colposcopy image and present a satisfactory visual effect. Significance. As a preprocessing method for computer-aided diagnosis systems, it can also improve physicians’ accuracy in clinical diagnosis.

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Section: Related Workmentioning

confidence: 99%

“…This paper is a further study based on our previous studies [ 30 ]. We propose a method to eliminate SR regions based on the integration of global and local features in colposcopic images, which solves the problem that SR regions cannot be wholly eliminated to retain the texture as much as possible.…”

Section: Related Workmentioning

confidence: 99%

Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

Wang

et al. 2021

Journal of Healthcare Engineering

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“…However, the quality of the reconstruction strongly depends on the initial parameters of the algorithm. The authors of [23] proposed inpainting of SRs in colposcopic images with an exemplar-based method.…”

Section: Related Workmentioning

confidence: 99%

Specular reflections removal in colposcopic images based on neural networks: Supervised training with no ground truth previous knowledge

Jimenez-Martin,

Pérez,

Asteasuainzarra

et al. 2021

Preprint

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Cervical cancer is a malignant tumor that seriously threatens women's health, and is one of the most common that affects women worldwide. For its early detection, colposcopic images of the cervix are used for searching for possible injuries or abnormalities. An inherent characteristic of these images is the presence of specular reflections (brightness) that make it difficult to observe some regions, which might imply misdiagnosis. In this paper, a new strategy based on neural networks is introduced for eliminating specular reflections and estimating the unobserved anatomical cervix portion under the bright zones. For overcoming the fact that the ground truth corresponding to the specular reflection regions is always unknown, the new strategy proposes the supervised training of a neural network to learn how to restore any hidden regions of colposcopic images. Once the specular reflections are identified, they are removed from the image, and the previously trained network is used to fulfill these deleted areas. The quality of the processed images was evaluated quantitatively and qualitatively. In 21 of the 22 evaluated images, the detected specular reflections were eliminated, whereas, in the remaining one, these reflections were almost completely eliminated. The distribution of the colors and the content of the restored images are similar to those of the originals. The evaluation carried out by a specialist in Cervix Pathology concluded that, after eliminating the specular reflections, the anatomical and physiological elements of the cervix are observable in the restored images, which facilitates the medical diagnosis of cervical pathologies. Our method has the potential to improve the early detection of cervical cancer.

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“…Specifically, it may potentially affect the doctor’s visual experience, deviate the attention of doctors from actual clinical goals, and hinder the tracking of surgical equipment [ 4 ]. In addition, through vaginal mirror screening for cervical cancer, specular reflection may change the appearance of the tissue and interfere with the performance of the segmentation of the cervical lesions area algorithm [ 5 , 6 , 7 ]. In colonoscopy imaging research, the detection of polyps and colorectal cancer may be affected by specular reflection [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…the highlights can no longer be considered sparse parts, and the method does not work well. Image inpainting-based methods [ 6 , 7 , 8 , 19 , 27 , 28 , 29 , 30 , 31 ] first detect the highlighted regions in the image and then inpaint with the most similar parts in the image. It can restore the specular regions under certain conditions, but it may be unreasonable for the reconstruction of human tissue structure, or the recovery effect for large-area specular regions is not good, or the algorithm operation efficiency is low.…”

Section: Introductionmentioning

confidence: 99%

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

Nie

Chen

et al. 2023

Sensors

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Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images.

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Detection and Inpainting of Specular Reflection in Colposcopic Images with Exemplar-based Method

Cited by 9 publications

References 15 publications

Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

Integration of Global and Local Features for Specular Reflection Inpainting in Colposcopic Images

Specular reflections removal in colposcopic images based on neural networks: Supervised training with no ground truth previous knowledge

Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

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