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Open AccessArticle

A Method for Local Contrast Enhancement of Endoscopic Images Based on Color Tensor Transformation into a Matrix of Color Vectors’ Modules Using a Sliding Window

by Roumen Kountchev, Alexander Bekiarski, Rumen Mironov and Snezhana Pleshkova

Symmetry 2022, 14(12), 2582; https://doi.org/10.3390/sym14122582 - 6 Dec 2022

Cited by 1 | Viewed by 2461

Abstract

A new method aimed at endoscopic color images’ local contrast enhancement is proposed, based on local sliding histogram equalization with adaptive threshold limitation, color distortions correction, and image brightness preservation. For this, the original RGB image, represented as a tensor of size M × N × 3, is transformed into a matrix of size M × N, composed by the color vectors’ modules. As a result of local contrast enhancement, the obtained color vectors are symmetrical in respect of the input ones, because they satisfy the requirement for invariance after rotation. To enhance the local contrast, recursive local histogram equalization with adaptive thresholding is applied to each matrix element. This threshold divides the histogram into two regions of equal areas. A new metric for local contrast enhancement evaluation based on the mean square difference entropy is proposed. The presented new method is characterized by low computational complexity, due to the lack of direct and inverse color conversion and the possibility for adaptive local contrast enhancement, which is essential for accurate medical diagnosis based on endoscopic images analysis. In addition, the presented method performs both the correction of color distortions and the brightness preservation of each pixel. Full article

(This article belongs to the Section Computer)

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10 pages, 2172 KB

Open AccessArticle

An Improved Method of Polyp Detection Using Custom YOLOv4-Tiny

by Mukhtorov Doniyorjon, Rakhmonova Madinakhon, Muksimova Shakhnoza and Young-Im Cho

Appl. Sci. 2022, 12(21), 10856; https://doi.org/10.3390/app122110856 - 26 Oct 2022

Cited by 22 | Viewed by 3268

Abstract

Automatic detection of Wireless Endoscopic Images can avoid dangerous possible diseases such as cancers. Therefore, a number of articles have been published on different methods to enhance the speed of detection and accuracy. We also present a custom version of the YOLOv4-tiny for Wireless Endoscopic Image detection and localization that uses a You Only Look Once (YOLO) version to enhance the model accuracy. We modified the YOLOv4-tiny model by replacing the CSPDarknet-53-tiny backbone structure with the Inception-ResNet-A block to enhance the accuracy of the original YOLOv4-tiny. In addition, we implemented a new custom data augmentation method to enhance the data quality, even for small datasets. We focused on maintaining the color of medical images because the sensitivity of medical images can affect the efficiency of the model. Experimental results showed that our proposed method obtains 99.4% training accuracy; compared with the previous models, this is more than a 1.2% increase. An original model used for both detection and the segmentation of medical images may cause a high error rate. In contrast, our proposed model could eliminate the error rate of the detection and localization of disease areas from wireless endoscopic images. Full article

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